- 1
- Allcott, H., and N. Wozny (2010), “Gasoline Prices, Fuel Economy, and the Energy Paradox”, mimeo, MIT.

Total in-text references: 2- In-text reference with the coordinate start=6307
- Prefix
- Section 4 studies the extent to which the nominal price of oil and the real price of oil are predictable based on macroeconomic aggregates. We document strong evidence of predictability 1 See, e.g., Kahn (1986), Davis and Kilian (2010). 2 See, e.g.,
- Exact
- Goldberg (1998), Allcott and Wozny (2010), Busse, Knittel and Zettelmeyer (2010), Kellogg (2010).
- Suffix
- in population. Predictability in population, however, need not translate into out-of-sample forecastability. The latter question is the main focus of sections 5 through 8. In sections 5, 6 and 7, we compare a wide range of out-of-sample forecasting methods for the nominal price of oil.

- In-text reference with the coordinate start=93372
- Prefix
- A variety of modeling strategies has been explored, often with widely different results. Candidates include ARIMA models, no-change forecasts, oil futures prices and gasoline futures prices (see, e.g.,
- Exact
- Kahn 1986; Davis and Kilian 2010; Allcott and Wozny 2010).
- Suffix
- The issue is not only one of finding a forecasting method that achieves the smallest possible out-of-sample forecast error, but of understanding how consumers form their price expectations. An obvious concern is that actual consumer expectations may differ from the predictions generated by the forecasting methods considered so far.

- In-text reference with the coordinate start=6307
- 2
- Almoguera, P.A., Douglas, C., and A.M. Herrera (2010), “Testing for the Cartel in OPEC: Noncooperative Collusion or Just Noncooperative?”, mimeo, Department of Economics, Michigan State University.

Total in-text references: 1- In-text reference with the coordinate start=21073
- Prefix
- First, there is little evidence to support the notion that OPEC has been successfully acting as a cartel in the 1970s and early 1980s, and the role of OPEC has diminished further since 1986 (see, e.g.,
- Exact
- Skeet 1988; Smith 2005; Almoguera, Douglas and Herrera 2010).
- Suffix
- Second, even if we were to accept the notion that an OPEC cartel sets the nominal price of oil, economic theory predicts that this cartel price will endogenously respond to U.S. macroeconomic conditions.

- In-text reference with the coordinate start=21073
- 3
- Alquist, R., and L. Kilian (2010), “What Do We Learn from the Price of Crude Oil Futures?” Journal of Applied Econometrics, 25, 539-573.

Total in-text references: 6- In-text reference with the coordinate start=57303
- Prefix
- Such attitudes have persisted notwithstanding recent empirical evidence to the contrary and notwithstanding the development of theoretical models aimed at explaining the lack of predictive ability of oil futures prices and spreads (see, e.g.,
- Exact
- Knetsch 2007; Alquist and Kilian 2010).
- Suffix
- Interestingly, the conventional wisdom in macroeconomics and finance is at odds with long-held views about storable commodities in agricultural economics. For example, Peck (1985) emphasized that “expectations are reflected nearly equally in current and in futures prices.

- In-text reference with the coordinate start=59211
- Prefix
- The NYMEX light sweet crude contract is the most liquid and largest volume market for crude oil trading. more strongly influenced by these anticipations than are spot prices”. The next section investigates the empirical merits of these competing views in the context of oil markets. 5.1. Forecasting Methods Based on Monthly Oil Futures Prices
- Exact
- Alquist and Kilian (2010)
- Suffix
- recently provided a comprehensive evaluation of the forecast accuracy of models based on monthly oil futures prices using data ending in 2007.2. Below we update their analysis until 2009.12 and expand the range of alternative forecasting models under consideration.18 In this subsection, attention is limited to forecast horizons of up to one year.

- In-text reference with the coordinate start=61264
- Prefix
- The simplest model is: () | ˆ1/ln()h SthttttSFS, 1, 3, 6, 9, 1 2h (3) To allow for the possibility that the spread may be a biased predictor, it is common to relax the 18 Because the Datastream data for the daily WTI spot price of oil used in
- Exact
- Alquist and Kilian (2010)
- Suffix
- were discontinued, we rely instead on data from the Energy Information Administration. As a result the estimation window for the forecast comparison is somewhat shorter in some cases than in Alquist and Kilian (2010). assumption of a zero intercept: ()|ˆ1/ˆln()htttthtSSFS, 1, 3, 6, 9, 12h (4) Alternatively, one can relax the proportionality restriction:

- In-text reference with the coordinate start=61481
- Prefix
- predictor, it is common to relax the 18 Because the Datastream data for the daily WTI spot price of oil used in Alquist and Kilian (2010) were discontinued, we rely instead on data from the Energy Information Administration. As a result the estimation window for the forecast comparison is somewhat shorter in some cases than in
- Exact
- Alquist and Kilian (2010).
- Suffix
- assumption of a zero intercept: ()|ˆ1/ˆln()htttthtSSFS, 1, 3, 6, 9, 12h (4) Alternatively, one can relax the proportionality restriction: () | ˆ1/ˆln()h SthttttSFS, 1, 3, 6, 9, 12h (5) Finally, we can relax both the unbiasedness and proportionality restrictions: ()|ˆ1/ˆˆln()htttthtSSFS, 1, 3, 6, 9, 12h.

- In-text reference with the coordinate start=66542
- Prefix
- We conclude that there is no compelling evidence that, over this sample period, monthly oil futures prices were more accurate predictors of the nominal price of oil than simple nochange forecasts. Put differently, a forecaster using the most recent spot price would have done just as well in forecasting the nominal price of oil. This finding is broadly consistent with the empirical results in
- Exact
- Alquist and Kilian (2010). To
- Suffix
- the extent that some earlier studies have reported evidence more favorable to oil futures prices, the difference in results can be traced to the use of shorter samples. 19 5.2. Other Forecasting Methods The preceding subsection demonstrated that simple no-change forecasts of the price of oil tend to be as accurate in the MSPE sense as forecasts based on oil futures prices, but this does

- In-text reference with the coordinate start=86235
- Prefix
- Long-Horizon Forecasts of the Nominal Price of Oil For oil industry managers facing investment decisions or for policymakers pondering the medium-term economic outlook a horizon of one year is too short. Crude oil futures may have maturities as long as seven years. Notwithstanding the low liquidity of oil futures markets at such long horizons, documented in
- Exact
- Alquist and Kilian (2010),
- Suffix
- it is precisely these long horizons that many policymakers focus on. For example, Greenspan (2004a) explicitly referred to the 6-year oil futures contract in assessing effective long-term supply prices.

- In-text reference with the coordinate start=57303
- 4
- Anatolyev, S. (2007), “Inference about Predictive Ability When There Are Many Predictors,” mimeo, New Economic School, Moscow.

Total in-text references: 1- In-text reference with the coordinate start=116183
- Prefix
- The tests for directional accuracy are not affected, of course. 30 The size problem of conventional tests of equal predictive accuracy gets worse, when the number of extra predictors under the alternative grows large relative to the sample size. This point has also been discussed in a much simpler context by
- Exact
- Anatolyev (2007)
- Suffix
- who shows that modifying conventional test statistics for equal predictive accuracy may remove these size distortions. Related results can be found in Calhoun (2010) who shows that standard tests of equal predictive accuracy for nested models such as Clark and McCracken (2001) or Clark and West (2007) will choose the larger model too often when the smaller model is more accurate in out-of-sampl

- In-text reference with the coordinate start=116183
- 5
- Anderson, S., Kellogg, R., and J. Sallee (2010), “What Do Consumers Know (or Think They Know) About the Price of Gasoline?” mimeo, Department of Economics, University of Michigan.

Total in-text references: 4- In-text reference with the coordinate start=9305
- Prefix
- We evaluate this survey forecast of the nominal retail price of gasoline against the no-change forecast benchmark. We also contrast this survey forecast with the price of the corresponding futures contracts. Following
- Exact
- Anderson, Kellogg and Sallee (2010),
- Suffix
- we document that, after controlling for inflation, long-term household gasoline price expectations are well approximated by a random walk. This finding has immediate implications for modeling purchases of energy-intensive consumer durables.

- In-text reference with the coordinate start=93936
- Prefix
- An obvious concern is that actual consumer expectations may differ from the predictions generated by the forecasting methods considered so far. Unfortunately, time series data on consumer expectations of gasoline prices are rare, which has prevented a systematic investigation of this important question. Recently,
- Exact
- Anderson, Kellogg and Sallee (2010)
- Suffix
- obtained a previously unused data set from the Michigan Survey of Consumers on U.S. households’ expectations of gasoline prices. The survey asks consumers about how many cents per gallon they think gasoline prices will increase or decrease during the next five years compared to now.

- In-text reference with the coordinate start=170086
- Prefix
- If volatility at the economically relevant horizon is constant by construction, it cannot explain variation in real activity over time, suggesting that survey data may be better suited for characterizing changes in forecast uncertainty over time. Some progress in this direction may be expected from ongoing work conducted by
- Exact
- Anderson, Kellogg and Sallee (2010)
- Suffix
- based on the distribution of Michigan consumer expectations of 5-year-ahead gasoline prices. For further discussion of this point also see Kilian and Vigfusson (2010b). 12.3. Quantifying Oil Price Risks Although oil price volatility shifts play an important role in discussions of the impact of oil price shocks, it is important to keep in mind that volatility measures are not in general usefu

- In-text reference with the coordinate start=183807
- Prefix
- Models that incorporate information about such spreads or about the underlying determinants of demand have the potential of improving forecasts of the price of a given grade of crude oil. A second issue of interest is the role played by heterogenous oil price and gasoline price expectations in modeling the demand for energy-intensive durables (see
- Exact
- Anderson, Kellogg and Sallee 2010).
- Suffix
- There is strong evidence that not all households share the same expectations, casting doubt on standard rational expectations models with homogeneous agents. This also calls into question the use of a single price forecast in modeling purchasing decisions in the aggregate.

- In-text reference with the coordinate start=9305
- 7
- Bachmeier, L., Li, Q., and D. Liu (2008), “Should Oil Prices Receive So Much Attention? An Evaluation of the Predictive Power of Oil Prices for the US Economy,” Economic Inquiry, 46, 528-539.

Total in-text references: 3- In-text reference with the coordinate start=160595
- Prefix
- Nonparametric Approaches Our approach in this section has been parametric. Alternatively, one could have used nonparametric econometric models to investigate the forecasting ability of the price of oil for real GDP. In related work,
- Exact
- Bachmeier, Li and Liu (2008)
- Suffix
- used the integrated conditional moment test of Corradi and Swanson (2002, 2007) to investigate whether oil prices help forecast real GDP growth one-quarter ahead. The advantage of this approach is that – while imposing linearity under the null – it allows for general nonlinear models under the alternative; the disadvantage is that the test is less powerful than the parametric approach if the p

- In-text reference with the coordinate start=161413
- Prefix
- The p-value for percent changes in the WTI price of crude oil is 0.77. Similar results are obtained for real net increases and for percent changes in the real WTI price. These findings are broadly consistent with ours.
- Exact
- Bachmeier et al. (2008)
- Suffix
- also report qualitatively similar results using a number of fully nonparametric approaches. An obvious caveat is that their analysis is based on data since 1949, which is not appropriate for the reasons discussed earlier, and ends before the 2008/09 recession.

- In-text reference with the coordinate start=164113
- Prefix
- The upper panel of Figure 15 shows the 1-month implied volatility time series for 2001.1-2009.12, computed from daily CRB data, following the same procedure as for the spot and futures prices in section 5. Alternatively, we may use daily percent changes in the nominal WTI price of oil to construct measures of realized volatility, as shown in the second panel of Figure 15 (see, e.g.,
- Exact
- Bachmeier, Li and Liu 2008).
- Suffix
- Finally, yet another measure of volatility can be constructed from parametric GARCH or stochastic volatility models. The bottom panel of Figure 15 shows the 1-month-ahead conditional variance obtained from recursively estimated Gaussian GARCH(1,1) models. 33 The initial estimation period is 1974.12000.12.

- In-text reference with the coordinate start=160595
- 9
- Barsky, R.B., and L. Kilian (2002), “Do We Really Know that Oil Caused the Great Stagflation? A Monetary Alternative,” in: NBER Macroeconomics Annual 2001, B.S. Bernanke and K. Rogoff (eds.), MIT Press: Cambridge, MA, 137-183.

Total in-text references: 7- In-text reference with the coordinate start=21624
- Prefix
- This theoretical prediction is consistent with anecdotal evidence of OPEC oil producers raising the price of oil (or equivalently lowering oil production) in response to unanticipated U.S. inflation, low U.S. interest rates and the depreciation of the dollar. Moreover, as observed by
- Exact
- Barsky and Kilian (2002),
- Suffix
- economic theory predicts that the strength of the oil cartel itself (measured by the extent to which individual cartel members choose to deviate from cartel guidelines) will be positively related to the state of the global business cycle (see Green and Porter 1984).

- In-text reference with the coordinate start=31720
- Prefix
- On the one hand, one would expect the evidence of predictability to be stronger for oil price series that are unregulated (such as the refiners’ acquisition cost for imported crude oil) than for partially regulated domestic price series. On the other hand, to the extent that the 1973/74 oil price shock episode was driven by monetary factors, as proposed by
- Exact
- Barsky and Kilian (2002),
- Suffix
- one would expect stronger evidence in favor of such feedback from the WTI price series that includes this episode. There are several reasons to expect the dollar-denominated nominal price of oil to respond to changes in nominal U.

- In-text reference with the coordinate start=33338
- Prefix
- Given the general instability in the link from changes in monetary aggregates to inflation, one would not necessarily expect changes in monetary aggregates to have much predictive power for the price of oil, except perhaps in the 1970s (see
- Exact
- Barsky and Kilian 2002).
- Suffix
- Table 1a nevertheless shows that there is considerable lagged feedback 8 In the former case, the pre-1974.1 observations are only used as pre-sample observations. 9 It can be shown that similar results hold for the CPI excluding energy, albeit not for the CPI excluding food and energy. 10 For an earlier exposition of the role of mone

- In-text reference with the coordinate start=33806
- Prefix
- lagged feedback 8 In the former case, the pre-1974.1 observations are only used as pre-sample observations. 9 It can be shown that similar results hold for the CPI excluding energy, albeit not for the CPI excluding food and energy. 10 For an earlier exposition of the role of monetary factors in determining the price of oil see
- Exact
- Barsky and Kilian (2002).
- Suffix
- Both Barsky and Kilian (2002) and Gillman and Nakov (2009) view the shifts in U.S. inflation in the early 1970s as caused by persistent changes in the growth rate of the money supply, but there are important differences in emphasis.

- In-text reference with the coordinate start=33837
- Prefix
- 8 In the former case, the pre-1974.1 observations are only used as pre-sample observations. 9 It can be shown that similar results hold for the CPI excluding energy, albeit not for the CPI excluding food and energy. 10 For an earlier exposition of the role of monetary factors in determining the price of oil see Barsky and Kilian (2002). Both
- Exact
- Barsky and Kilian (2002) and Gillman and Nakov (2009)
- Suffix
- view the shifts in U.S. inflation in the early 1970s as caused by persistent changes in the growth rate of the money supply, but there are important differences in emphasis. Whereas Barsky and Kilian stress the real effects of unanticipated monetary expansions on real domestic output, on the demand for oil and hence on the real price of oil, Gillman and Nakov stress that the relative price of

- In-text reference with the coordinate start=43403
- Prefix
- Thus, regressions on long time spans of real exchange rate data produce average estimates that by construction are not informative about the speed of adjustment in the Bretton Woods system. 14 For a review of this literature see
- Exact
- Barsky and Kilian (2002).
- Suffix
- difficult to pin down, especially at longer horizons, and that the relevant horizon for resource extraction is not clear. We therefore focus on the predictive power of fluctuations in real aggregate output.

- In-text reference with the coordinate start=104871
- Prefix
- of predictive models suggests that using the no-change forecast may lower the asymptotic MSPE even relative to the correctly specified non-random walk model, provided the local drift parameter governing the predictive relationship is close enough to zero (see, e.g., Inoue and Kilian (2004b), Clark and McCracken 2010). 26 The refiners’ acquisition cost was extrapolated back to 1973.2 as in
- Exact
- Barsky and Kilian (2002).
- Suffix
- selection (see Inoue and Kilian 2006; Marcellino, Stock and Watson 2006). We search over p0,...,12 . The forecast accuracy results are robust to allowing for a larger upper bound. There are no theoretical results in the forecasting literature on how to assess the null of equal predictive accuracy when comparing iterated AR or ARMA forecasts to the no-change forecast.

- In-text reference with the coordinate start=21624
- 10
- Basak, S., and A. Shapiro (2001), “Value-at-Risk Based Management: Optimal Policies and Asset Prices,” Review of Financial Studies, 14, 371–405.

Total in-text references: 1- In-text reference with the coordinate start=176829
- Prefix
- Even that target variance, however, is distinct 38 Measures of risk of this type were first introduced by Fishburn (1977), Holthausen (1981), Artzner, Delbaen, Eber and Heath (1999), and
- Exact
- Basak and Shapiro (2001)
- Suffix
- in the context of portfolio risk management and have become a standard tool in recent years (see, e.g., Engle and Brownlees 2010). For a general exposition of risk measures and risk management in a different context see Kilian and Manganelli (2007, 2008). from conventionally used measures of oil price volatility, defined as the variance about the sample mean of the predictive distribution.

- In-text reference with the coordinate start=176829
- 11
- Baumeister, C., and L. Kilian (2011), “Real-Time Forecasts of the Real Price of Oil,” mimeo, Department of Economics, University of Michigan.

Total in-text references: 4- In-text reference with the coordinate start=118617
- Prefix
- How imposing these real-time data constraints alters the relative accuracy of no-change benchmark model compared with VAR models is not clear a priori because both the benchmark model and the alternative model are affected. The first study to investigate this question is
- Exact
- Baumeister and Kilian (2011)
- Suffix
- who recently developed a real-time data set for the variables in question. They find (based on a data set extending until 2010.6) that VAR forecasting models of the type considered in this section can generate substantial improvements in real-time forecast accuracy.

- In-text reference with the coordinate start=121326
- Prefix
- Clearly, the real price of WTI crude oil is more difficult to forecast in the short run than the real U.S. refiners’ acquisition cost for imported crude oil. Broadly similar results would be obtained with real-time data (see
- Exact
- Baumeister and Kilian 2011).
- Suffix
- Unlike for the real refiners’ acquisition cost, the differences between real-time forecasts of the real WTI price and forecasts based on ex-post revised data tend to be small. 8.3. Restricted VAR Models Although the results for the unrestricted VAR models in Tables 13 and 15 are encouraging, there is reason to believe that alternative estimation methods may reduce the MSPE of the VAR forec

- In-text reference with the coordinate start=123989
- Prefix
- For example, model (1) with 12 lags yields MSPE reductions of 20% at horizon 1, 12% at horizon 3, and 3% at horizon 6 with no further gains at longer horizons. Model (1) with 24 lags yields gains of 20%, 12% and 1%, respectively. Again, it can be shown that similar gains in accuracy are feasible even using real-time data (see
- Exact
- Baumeister and Kilian 2011).
- Suffix
- In addition, such VAR models can also be useful for studying how baseline forecasts of the real price of oil must be adjusted under hypothetical forecasting scenarios, as illustrated in the next section.

- In-text reference with the coordinate start=126117
- Prefix
- In this section we illustrate how to generate such projections from the structural moving average representation of the VAR model of Kilian and Murphy (2010) estimated on data extending to 2009.8. The discussion closely follows
- Exact
- Baumeister and Kilian (2011).
- Suffix
- This model allows the identification of three structural shocks: (1) a shock to the flow of the production of crude oil (“flow supply shock), (2) a shock to the flow demand for crude oil and other industrial commodities (“flow demand shock”) that reflects unexpected fluctuations in the global business cycle, and (3) a shock to the demand for oil inventories arising from forward-looking behavio

- In-text reference with the coordinate start=118617
- 12
- Baumeister, C., and G. Peersman (2010), “Sources of the Volatility Puzzle in the Crude Oil Market,” mimeo, Department of Economics, Ghent University.

Total in-text references: 2- In-text reference with the coordinate start=26056
- Prefix
- Although Hamilton (2003) applied this transformation to the nominal price of oil, several other studies have recently explored models that apply the same transformation to the real price of oil (see, e.g., Kilian and Vigfusson 2010a; Herrera, Lagalo and Wada 2010). prior to 2003. This finding is also consistent with the empirical results in
- Exact
- Baumeister and Peersman (2010).
- Suffix
- For now we set aside all nonlinear transformations of the price of oil and focus on linear forecasting models for the nominal price of oil and for the real price of oil. Nonlinear joint forecasting models for U.

- In-text reference with the coordinate start=49836
- Prefix
- An alternative quarterly predictor that partially addresses these last two concerns is quarterly world industrial production from the U.N. Monthly Bulletin of Statistics. This series has recently been introduced by
- Exact
- Baumeister and Peersman (2010)
- Suffix
- in the context of modeling the demand for oil. Although there are serious methodological concerns regarding the construction of any such index, as discussed in Beyer, Doornik and Hendry (2001), one would expect this series to be a better proxy for global fluctuations in the demand for crude oil than U.

- In-text reference with the coordinate start=26056
- 13
- Bernanke, B.S. (1983), “Irreversibility, Uncertainty, and Cyclical Investment,” Quarterly Journal of Economics, 98, 85‐106.

Total in-text references: 1- In-text reference with the coordinate start=168014
- Prefix
- Real Oil Price Volatility Interest in the volatility of oil prices also has been prompted by research aimed at establishing a direct link from oil price volatility to business cycle fluctuations in the real economy. For example,
- Exact
- Bernanke (1983) and Pindyck (1991)
- Suffix
- showed that the uncertainty of the price of oil (measured by the volatility of the price of oil) matters for investment decisions if firms contemplate an irreversible investment, the cash flow of which depends on the price of oil.

- In-text reference with the coordinate start=168014
- 14
- Bernanke, B.S (2004), “Oil and the Economy,” Speech presented at Darton College, Albany, GA, http://www.federalreserve.gov/boarddocs/speeches/2004/20041021/default.htm

Total in-text references: 1- In-text reference with the coordinate start=86498
- Prefix
- Notwithstanding the low liquidity of oil futures markets at such long horizons, documented in Alquist and Kilian (2010), it is precisely these long horizons that many policymakers focus on. For example, Greenspan (2004a) explicitly referred to the 6-year oil futures contract in assessing effective long-term supply prices. For similar statements also see
- Exact
- Greenspan (2004b), Gramlich (2004) and Bernanke (2004).
- Suffix
- In this section we focus on forecasting the nominal price of oil at horizons up to seven years. It can be shown that the daily data are too sparse at horizons beyond one year to allow the construction of time series of end-of-month observations for oil futures prices.

- In-text reference with the coordinate start=86498
- 15
- Beyer, A., Doornik, J.A. and Hendry, D.F. (2001), “Constructing Historical Euro-Zone Data,” Economic Journal, 111, 308-327.

Total in-text references: 1- In-text reference with the coordinate start=50027
- Prefix
- This series has recently been introduced by Baumeister and Peersman (2010) in the context of modeling the demand for oil. Although there are serious methodological concerns regarding the construction of any such index, as discussed in
- Exact
- Beyer, Doornik and Hendry (2001),
- Suffix
- one would expect this series to be a better proxy for global fluctuations in the demand for crude oil than U.S. real GDP. Indeed, Table 2 shows strong evidence of Granger causality from world industrial production to the real WTI price in the full sample period for the LT model.

- In-text reference with the coordinate start=50027
- 16
- Bollerslev, T., Chou, R.Y., and K.F. Kroner (1992), “ARCH Modeling in Finance,” Journal of Econometrics, 52, 5-59.

Total in-text references: 1- In-text reference with the coordinate start=165593
- Prefix
- Given that oil is only one of many assets handled by portfolio managers, however, it is not clear that the GARCHin-Mean model for single-asset markets is appropriate in this context, while more general multivariate GARCH models are all but impossible to estimate reliably on the small samples available for our purposes (see, e.g.,
- Exact
- Bollerslev, Chou and Kroner 1992).
- Suffix
- 34 We deliberately focus on oil price volatility at the 1-month horizon. Although from an economic point of view volatility forecasting at longer horizons would be of great interest, the sparsity of options price data makes it difficult to extend the implied volatility approach to longer horizons.

- In-text reference with the coordinate start=165593
- 17
- Busse, M., Knittel, C., and F. Zettelmeyer (2010), “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing,” mimeo, Northwestern University.

Total in-text references: 1- In-text reference with the coordinate start=6307
- Prefix
- Section 4 studies the extent to which the nominal price of oil and the real price of oil are predictable based on macroeconomic aggregates. We document strong evidence of predictability 1 See, e.g., Kahn (1986), Davis and Kilian (2010). 2 See, e.g.,
- Exact
- Goldberg (1998), Allcott and Wozny (2010), Busse, Knittel and Zettelmeyer (2010), Kellogg (2010).
- Suffix
- in population. Predictability in population, however, need not translate into out-of-sample forecastability. The latter question is the main focus of sections 5 through 8. In sections 5, 6 and 7, we compare a wide range of out-of-sample forecasting methods for the nominal price of oil.

- In-text reference with the coordinate start=6307
- 18
- Calhoun, G. (2010), “Limit Theory for Comparing Overfit Models Out-of-Sample,” mimeo, Department of Economics, Iowa State University.

Total in-text references: 1- In-text reference with the coordinate start=116351
- Prefix
- This point has also been discussed in a much simpler context by Anatolyev (2007) who shows that modifying conventional test statistics for equal predictive accuracy may remove these size distortions. Related results can be found in
- Exact
- Calhoun (2010)
- Suffix
- who shows that standard tests of equal predictive accuracy for nested models such as Clark and McCracken (2001) or Clark and West (2007) will choose the larger model too often when the smaller model is more accurate in out-of-sample forecasts and also proposes alternative asymptotic approximations based on many predictors.

- In-text reference with the coordinate start=116351
- 19
- Carlton, A.B. (2010), “Oil Prices and Real-Time Output Growth,” mimeo, Department of Economics, University of Houston.

Total in-text references: 1- In-text reference with the coordinate start=159959
- Prefix
- This remains an open question at this point.32 32 Some preliminary evidence on this question has been provided by Ravazzolo and Rothman (2010) and by
- Exact
- Carlton (2010).
- Suffix
- It is not straightforward to compare their results to those in Tables 19 and 20, however. Not only is their analysis based on one-step-ahead real GDP growth forecasts from single-equation predictive models evaluated at the relevant forecasting horizon (rather than iterated forecasts from multivariate models), but it is based on a sample period that includes pre-1973 data. 11.3.

- In-text reference with the coordinate start=159959
- 21
- Clark, T.E., and M. McCracken (2001), “Tests of Equal Predictive Accuracy and Encompassing for Nested Models,” Journal of Econometrics, 105, 85-101.

Total in-text references: 2- In-text reference with the coordinate start=105322
- Prefix
- There are no theoretical results in the forecasting literature on how to assess the null of equal predictive accuracy when comparing iterated AR or ARMA forecasts to the no-change forecast. In particular, the standard tests discussed in
- Exact
- Clark and McCracken (2001, 2005)
- Suffix
- or Clark and West (2007) are only designed for direct forecasts. Below we assess the significance of the MSPE reductions based on bootstrap p-values for the MSPE ratio constructed under the null of a random walk model without drift. 27 The upper panel of Table 12 suggests that AR and ARMA models in log levels have lower recursive MSPE than the no-change forecast at short horizons.

- In-text reference with the coordinate start=116451
- Prefix
- This point has also been discussed in a much simpler context by Anatolyev (2007) who shows that modifying conventional test statistics for equal predictive accuracy may remove these size distortions. Related results can be found in Calhoun (2010) who shows that standard tests of equal predictive accuracy for nested models such as
- Exact
- Clark and McCracken (2001)
- Suffix
- or Clark and West (2007) will choose the larger model too often when the smaller model is more accurate in out-of-sample forecasts and also proposes alternative asymptotic approximations based on many predictors.

- In-text reference with the coordinate start=105322
- 22
- Clark, T.E., and M. McCracken (2005), “Evaluating Direct Multistep Forecasts,” Econometric Reviews, 24, 369-404.

Total in-text references: 1- In-text reference with the coordinate start=105322
- Prefix
- There are no theoretical results in the forecasting literature on how to assess the null of equal predictive accuracy when comparing iterated AR or ARMA forecasts to the no-change forecast. In particular, the standard tests discussed in
- Exact
- Clark and McCracken (2001, 2005)
- Suffix
- or Clark and West (2007) are only designed for direct forecasts. Below we assess the significance of the MSPE reductions based on bootstrap p-values for the MSPE ratio constructed under the null of a random walk model without drift. 27 The upper panel of Table 12 suggests that AR and ARMA models in log levels have lower recursive MSPE than the no-change forecast at short horizons.

- In-text reference with the coordinate start=105322
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- Clark, T.E., and M. McCracken (2010), “Nested Forecast Model Comparisons: A New Approach to Testing Equal Accuracy,” mimeo, Federal Reserve Bank of St. Louis.

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- Prefix
- It should be noted that commonly used tests of equal predictive accuracy for nested models (including the tests we rely on in this chapter) by construction are tests of the null of no predictability in population rather than tests of equal outof-sample MSPEs (see, e.g.,
- Exact
- Inoue and Kilian 2004a,b; Clark and McCracken 2010).
- Suffix
- This means that these tests will reject the null of equal predictive accuracy more often than they should under the null, suggesting caution in interpreting test results that are only marginally statistically significant.

- In-text reference with the coordinate start=104745
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- The local-to-zero asymptotic approximation of predictive models suggests that using the no-change forecast may lower the asymptotic MSPE even relative to the correctly specified non-random walk model, provided the local drift parameter governing the predictive relationship is close enough to zero (see, e.g.,
- Exact
- Inoue and Kilian (2004b), Clark and McCracken 2010).
- Suffix
- 26 The refiners’ acquisition cost was extrapolated back to 1973.2 as in Barsky and Kilian (2002). selection (see Inoue and Kilian 2006; Marcellino, Stock and Watson 2006). We search over p0,...,12 .

- In-text reference with the coordinate start=115350
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- Which model is the population model, of course, is irrelevant for the question of which model generates more accurate forecasts in finite samples, so we have to interpret this rejection with some caution. This type of insight recently has prompted the development of alternative tests of equal predictive accuracy based on local-to-zero asymptotic approximations to the predictive regression.
- Exact
- Clark and McCracken (2010)
- Suffix
- for the first time proposed a correctly specified test of the null of equal out-of-sample MSPEs. Their analysis is limited to direct forecasts from much simpler forecasting models, however, and cannot be applied in Table 13.30 This caveat suggests that we discount only marginally statistically significant rejections of the no predictability null hypothesis in Table 13 and focus on the highly

- In-text reference with the coordinate start=64607
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- Clark, T.E., and K.D. West (2007), “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models,” Journal of Econometrics, 138, 291-311.

Total in-text references: 2- In-text reference with the coordinate start=105359
- Prefix
- There are no theoretical results in the forecasting literature on how to assess the null of equal predictive accuracy when comparing iterated AR or ARMA forecasts to the no-change forecast. In particular, the standard tests discussed in Clark and McCracken (2001, 2005) or
- Exact
- Clark and West (2007)
- Suffix
- are only designed for direct forecasts. Below we assess the significance of the MSPE reductions based on bootstrap p-values for the MSPE ratio constructed under the null of a random walk model without drift. 27 The upper panel of Table 12 suggests that AR and ARMA models in log levels have lower recursive MSPE than the no-change forecast at short horizons.

- In-text reference with the coordinate start=116481
- Prefix
- This point has also been discussed in a much simpler context by Anatolyev (2007) who shows that modifying conventional test statistics for equal predictive accuracy may remove these size distortions. Related results can be found in Calhoun (2010) who shows that standard tests of equal predictive accuracy for nested models such as Clark and McCracken (2001) or
- Exact
- Clark and West (2007)
- Suffix
- will choose the larger model too often when the smaller model is more accurate in out-of-sample forecasts and also proposes alternative asymptotic approximations based on many predictors. None of the remedies is directly applicable in the context of Table 12, however. 8.1.2.

- In-text reference with the coordinate start=105359
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- Cooley, T.F., and S. LeRoy (1985), “Atheoretical Macroeconometrics: A Critique,” Journal of Monetary Economics, 16, 283-308.

Total in-text references: 2- In-text reference with the coordinate start=20344
- Prefix
- an episode as an oil price shock involving a doubling of the 4 For further discussion of the trade-offs between alternative oil price definitions from an economic point of view see Kilian and Vigfusson (2010b). 5 For a review of the relationship between the concepts of (strict) exogeneity and predictability in linear models see
- Exact
- Cooley and LeRoy (1985).
- Suffix
- nominal price of oil. Indeed, economic models of the impact of the price of oil on the U.S. economy correctly predict that such a nominal oil price shock should have no effect on the U.S. economy because theoretical models inevitably are specified in terms of the real price of oil, which has not changed in this example.

- In-text reference with the coordinate start=29742
- Prefix
- This line of reasoning is familiar from the analysis of stock and bond prices as well as exchange rates. 7 In the latter case, the endogeneity of the nominal price of oil with respect to the U.S. economy implies that lagged changes in U.S. macroeconomic aggregates have predictive power for the nominal price of oil in the post-1973 data (see, e.g.,
- Exact
- Cooley and LeRoy 1985).
- Suffix
- A recent study by Kilian and Vega (2010) helps resolve the question of which interpretation is more appropriate. Kilian and Vega find no evidence of systematic feedback from news about a wide range of U.

- In-text reference with the coordinate start=20344
- 27
- Corradi, V., and N.R. Swanson (2002), “A Consistent Test for Nonlinear Out of Sample Predictive Accuracy,” Journal of Econometrics, 110, 353-381.

Total in-text references: 1- In-text reference with the coordinate start=160671
- Prefix
- Alternatively, one could have used nonparametric econometric models to investigate the forecasting ability of the price of oil for real GDP. In related work, Bachmeier, Li and Liu (2008) used the integrated conditional moment test of
- Exact
- Corradi and Swanson (2002, 2007) to
- Suffix
- investigate whether oil prices help forecast real GDP growth one-quarter ahead. The advantage of this approach is that – while imposing linearity under the null – it allows for general nonlinear models under the alternative; the disadvantage is that the test is less powerful than the parametric approach if the parametric structure is known.

- In-text reference with the coordinate start=160671
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- Corradi, V., and N.R. Swanson (2007), “Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes,” International Economic Review, 48, 67-109.

Total in-text references: 1- In-text reference with the coordinate start=160671
- Prefix
- Alternatively, one could have used nonparametric econometric models to investigate the forecasting ability of the price of oil for real GDP. In related work, Bachmeier, Li and Liu (2008) used the integrated conditional moment test of
- Exact
- Corradi and Swanson (2002, 2007) to
- Suffix
- investigate whether oil prices help forecast real GDP growth one-quarter ahead. The advantage of this approach is that – while imposing linearity under the null – it allows for general nonlinear models under the alternative; the disadvantage is that the test is less powerful than the parametric approach if the parametric structure is known.

- In-text reference with the coordinate start=160671
- 29
- Dargay, J.M., and D. Gately (2010), “World Oil Demand’s Shift toward Faster Growing and Less Price-Responsive Products and Regions,” Energy Policy, 38, 6261-6277.

Total in-text references: 1- In-text reference with the coordinate start=174571
- Prefix
- functions ()lthat encompasses the two empirical examples above is: 36 A similar irreversible shift in OECD demand occurred after the oil price shocks of the 1970s when fuel oil was increasingly replaced by natural gas. The fuel oil market never recovered, even as the price of this fuel fell dramatically in the 1980s and 1990s (see
- Exact
- Dargay and Gately 2010).
- Suffix
- 37 The threshold of $120 in this example follows from adjusting the cost estimates for shale oil production in Farrell and Brandt (2006) for the cumulative inflation rate since 2000. lRifR RR aRR ifRR () ()0 (1) () thth thth aR RifRR thth where thRdenotes the real price of oil in dollars hperiods from date ,t 01ais the weight attached to downside ris

- In-text reference with the coordinate start=174571
- 30
- Davies, P. (2007), “What’s the Value of an Energy Economist?” Presentation at the 30th Annual Conference of the International Association for Energy Economics, Wellington, New Zealand, February 18.

Total in-text references: 1- In-text reference with the coordinate start=75749
- Prefix
- This result is consistent with common views among oil experts. For example, Peter Davies, chief economist of British Petroleum, has noted that “we cannot forecast oil prices with any degree of accuracy over any period whether short or long” (see
- Exact
- Davies 2007).
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- 5.2.4. Predictors Based on Other Nominal Prices The evidence on Granger causality in section 4.1.2 suggests that some asset prices may have predictive power in real time for the nominal price of oil.

- In-text reference with the coordinate start=75749
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- Dvir, E., and K. Rogoff (2010), “Three Epochs of Oil,” mimeo, Harvard University.

Total in-text references: 1- In-text reference with the coordinate start=17276
- Prefix
- Shifting the starting date of the OPEC period to 1974.1, in contrast, implies a considerable increase in volatility after 1985. Extending the ending date of the OPEC period to include the price collapse in 1986 induced by 3 In related work,
- Exact
- Dvir and Rogoff (2010)
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- present formal evidence of a structural break in the process driving the annual real price of oil in 1973. Given this evidence of instability, combining pre- and post-1973 real oil price data is not a valid option.

- In-text reference with the coordinate start=17276
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- Edelstein, P., and L. Kilian (2009), “How Sensitive are Consumer Expenditures to Retail Energy Prices?” Journal of Monetary Economics, 56, 766-779.

Total in-text references: 5- In-text reference with the coordinate start=4295
- Prefix
- For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. For example, Hamilton (2009), building on the analysis in
- Exact
- Edelstein and Kilian (2009),
- Suffix
- provides evidence that the recession of late 2008 was amplified and preceded by an economic slowdown in the automobile industry and a deterioration in consumer sentiment. Thus, more accurate forecasts of the price of oil have the potential of improving forecast accuracy for a wide range of macroeconomic outcomes and of improving macroeconomic policy responses.

- In-text reference with the coordinate start=96585
- Prefix
- The evidence in Figure 6 supports the view that the no-change forecast for the real price of gasoline is a better proxy than alternative forecasting models for modeling durables purchases. That evidence also is of interest more generally, given the finding in
- Exact
- Edelstein and Kilian (2009)
- Suffix
- that fluctuations in retail energy prices are dominated by fluctuations in gasoline prices. Finally, the absence of money illusion in households’ gasoline price forecasts is of independent interest. An out-of-sample forecast accuracy comparison between the survey forecast and the no- change forecast of the nominal price of gasoline shows that survey data are quite accurate with an MSPE ratio

- In-text reference with the coordinate start=141297
- Prefix
- This suggests that we replace the percent change in the real price of oil in the linear VAR model by the percent change in the real price of oil weighted by the time-varying share of oil in domestic expenditures, building on the analysis in
- Exact
- Edelstein and Kilian (2009). Hamilton (2009)
- Suffix
- reported some success in employing a similar strategy.31 Another source of time variation may be changes in the composition of the underlying oil demand and oil supply shocks, as discussed in Kilian (2009).

- In-text reference with the coordinate start=143161
- Prefix
- 3 [,], netyr possibly sytt 31 In related work, Ramey and Vine (2010) propose an alternative adjustment to the price of gasoline that reflects the time cost of queuing in gasoline markets during the 1970s. That adjustment as well serves to remove a nonlinearity in the transmission process. Both the nonlinearity postulated in
- Exact
- Edelstein and Kilian (2009) and
- Suffix
- that postulated in Ramey and Vine (2010) is incompatible with the specific nonlinearity embodied in the models of Mork (1989) and Hamilton (1996, 2003). In fact, the aforementioned papers rely on linear regressions after adjusting the energy price data. augmented by other variables.

- In-text reference with the coordinate start=172725
- Prefix
- In fact, it can be shown that risk measures are not only quantitatively different from volatility measures, but in practice may move in the opposite direction. Likewise, a consumer of retail motor gasoline (and hence indirectly of crude oil) is likely to be concerned with the price of gasoline exceeding what he can afford to spend each month (see
- Exact
- Edelstein and Kilian 2009).
- Suffix
- The threshold at which consumers might trade in their SUV for a more energy-efficient car is near $3 a gallon perhaps. The threshold at which commuters may decide to relocate closer to their place of work might be at a price near $5 a gallon.

- In-text reference with the coordinate start=4295
- 36
- Elder, J., and A. Serletis (2010), “Oil Price Uncertainty,” Journal of Money, Credit and Banking, 42, 1138-1159

Total in-text references: 1- In-text reference with the coordinate start=169401
- Prefix
- Measuring the volatility of the real price of oil at such long forecast horizons is inherently difficult given how short the available time series are, and indeed researchers in practice have typically asserted rather than measured these shifts in real price volatility or they have treated short-horizon volatility as a proxy for longer-horizon volatility (see, e.g.,
- Exact
- Elder and Serletis 2010).
- Suffix
- 35 This approach is unlikely to work. Standard monthly or quarterly GARCH model cannot be used to quantify changes in the longerrun expected volatility of the real price of oil because GARCH forecasts of the conditional variance quickly revert to their time invariant unconditional expectation, as the forecasting horizon increases.

- In-text reference with the coordinate start=169401
- 37
- Elliott, G., and A. Timmermann (2008), “Economic Forecasting,” Journal of Economic Literature, 46, 3-56.

Total in-text references: 1- In-text reference with the coordinate start=63765
- Prefix
- The forecast evaluation period is 1991.1-2009.12 with suitable adjustments, as the forecast horizon is varied. The assessment of which forecasting model is most accurate may depend on the loss function of the forecaster (see
- Exact
- Elliott and Timmermann 2008).
- Suffix
- We report results for the MSPE and the relative frequency with which a forecasting model correctly predicts the sign of the change in the spot price based on the success ratio statistic of Pesaran and Timmermann (2009).

- In-text reference with the coordinate start=63765
- 38
- Engle, R.F., and C.T. Brownlees (2010), “Volatility, Correlation and Tails for Systemic Risk Measurement,” mimeo, Stern School of Business, New York University.

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- Prefix
- Even that target variance, however, is distinct 38 Measures of risk of this type were first introduced by Fishburn (1977), Holthausen (1981), Artzner, Delbaen, Eber and Heath (1999), and Basak and Shapiro (2001) in the context of portfolio risk management and have become a standard tool in recent years (see, e.g.,
- Exact
- Engle and Brownlees 2010).
- Suffix
- For a general exposition of risk measures and risk management in a different context see Kilian and Manganelli (2007, 2008). from conventionally used measures of oil price volatility, defined as the variance about the sample mean of the predictive distribution.

- In-text reference with the coordinate start=176958
- 39
- Farrell, A.E., and A.R. Brandt (2006), “Risks of the Oil Transition,” Environmental Research Letters, 1, 1-6.

Total in-text references: 1- In-text reference with the coordinate start=174707
- Prefix
- The fuel oil market never recovered, even as the price of this fuel fell dramatically in the 1980s and 1990s (see Dargay and Gately 2010). 37 The threshold of $120 in this example follows from adjusting the cost estimates for shale oil production in
- Exact
- Farrell and Brandt (2006)
- Suffix
- for the cumulative inflation rate since 2000. lRifR RR aRR ifRR () ()0 (1) () thth thth aR RifRR thth where thRdenotes the real price of oil in dollars hperiods from date ,t 01ais the weight attached to downside risks, and 0 and 0 measure the user’s degree of risk aversion.

- In-text reference with the coordinate start=174707
- 40
- Fishburn, P.C. (1977), “Mean-Risk Analysis with Risk Associated with Below-Target Returns,” American Economic Review, 67, 116–26.

Total in-text references: 1- In-text reference with the coordinate start=176748
- Prefix
- In particular, if and only if the loss function is quadratic and symmetric about zero, the variance of the price of oil about zero provides an adequate summary statistic for the risk in oil price forecasts. Even that target variance, however, is distinct 38 Measures of risk of this type were first introduced by
- Exact
- Fishburn (1977), Holthausen (1981),
- Suffix
- Artzner, Delbaen, Eber and Heath (1999), and Basak and Shapiro (2001) in the context of portfolio risk management and have become a standard tool in recent years (see, e.g., Engle and Brownlees 2010).

- In-text reference with the coordinate start=176748
- 41
- Giannone, D., Lenza, M. and G. Primiceri (2010), “Prior Selection for Vector Autoregressions,” mimeo, Department of Economics, Free University of Brussels.

Total in-text references: 2- In-text reference with the coordinate start=122107
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- In the VAR model at hand a natural starting point would be to shrink all lagged parameters toward zero under the maintained assumption of stationarity. This leaves open the question of how to determine the weights of the prior relative to the information in the likelihood.
- Exact
- Giannone, Lenza and Primiceri (2010)
- Suffix
- recently proposed a simple and theoretically founded data-based method for the selection of priors in recursively estimated Bayesian VARs (BVARs). Their recommendation is to select priors using the marginal data density (i.e., the likelihood function integrated over the model parameters), which only depends on the hyperparameters that characterize the relative weight of the prior and the info

- In-text reference with the coordinate start=123109
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- Table 16 compares the forecasting accuracy of this approach with that of the unrestricted VAR models considered in Tables 13 and 15. In all cases, we shrink the model parameters toward a white noise prior mean with the desired degree of shrinkage being determined by the data-based procedure in
- Exact
- Giannone et al. (2010).
- Suffix
- For models with 12 lags, there is no strong evidence that shrinkage estimation reduces the MSPE. Although there are some cases in which imposing Bayesian priors reduces the MSPE slightly, in other cases it increases the MSPE slightly.

- In-text reference with the coordinate start=122107
- 42
- Gillman, M., and A. Nakov (2009), “Monetary Effects on Nominal Oil Prices,” North American Journal of Economics and Finance, 20, 239-254.

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- If the U.S. money supply unexpectedly doubles, for example, then, according to standard macroeconomic models, so will all nominal prices denominated in dollars (including the nominal price of oil), leaving the relative price or real price of crude oil unaffected (see
- Exact
- Gillman and Nakov 2009).
- Suffix
- Clearly, one would not want to interpret such an episode as an oil price shock involving a doubling of the 4 For further discussion of the trade-offs between alternative oil price definitions from an economic point of view see Kilian and Vigfusson (2010b). 5 For a review of the relationship between the concepts of (strict) exogeneity

- In-text reference with the coordinate start=32104
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- There are several reasons to expect the dollar-denominated nominal price of oil to respond to changes in nominal U.S. macroeconomic aggregates. One channel of transmission is purely monetary and operates through U.S. inflation. For example,
- Exact
- Gillman and Nakov (2009)
- Suffix
- stress that changes in the nominal price of oil must occur in equilibrium just to offset persistent shifts in U.S. inflation, given that the price of oil is denominated in dollars. Indeed, the Granger causality tests in Table 1a indicate highly significant lagged feedback from U.

- In-text reference with the coordinate start=32542
- Prefix
- Indeed, the Granger causality tests in Table 1a indicate highly significant lagged feedback from U.S. headline CPI inflation to the percent change in the nominal WTI price of oil for the full sample, consistent with the findings in
- Exact
- Gillman and Nakov (2009).
- Suffix
- The evidence for the other oil price series is somewhat weaker with the exception of the refiners’ acquisition cost for imported crude oil, but that result may simply reflect a loss of power when the sample size is shortened.9 Gillman and Nakov view changes in inflation in the post-1973 period as rooted in persistent changes in the growth rate of money. 10 Thus, an alternative approach of tes

- In-text reference with the coordinate start=32984
- Prefix
- oil price series is somewhat weaker with the exception of the refiners’ acquisition cost for imported crude oil, but that result may simply reflect a loss of power when the sample size is shortened.9 Gillman and Nakov view changes in inflation in the post-1973 period as rooted in persistent changes in the growth rate of money. 10 Thus, an alternative approach of testing the hypothesis of
- Exact
- Gillman and Nakov (2009)
- Suffix
- is to focus on Granger causality from monetary aggregates to the nominal price of oil. Given the general instability in the link from changes in monetary aggregates to inflation, one would not necessarily expect changes in monetary aggregates to have much predictive power for the price of oil, except perhaps in the 1970s (see Barsky and Kilian 2002).

- In-text reference with the coordinate start=33837
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- 8 In the former case, the pre-1974.1 observations are only used as pre-sample observations. 9 It can be shown that similar results hold for the CPI excluding energy, albeit not for the CPI excluding food and energy. 10 For an earlier exposition of the role of monetary factors in determining the price of oil see Barsky and Kilian (2002). Both
- Exact
- Barsky and Kilian (2002) and Gillman and Nakov (2009)
- Suffix
- view the shifts in U.S. inflation in the early 1970s as caused by persistent changes in the growth rate of the money supply, but there are important differences in emphasis. Whereas Barsky and Kilian stress the real effects of unanticipated monetary expansions on real domestic output, on the demand for oil and hence on the real price of oil, Gillman and Nakov stress that the relative price of

- In-text reference with the coordinate start=38649
- Prefix
- Reconciling the Pre- and Post-1973 Evidence on Predictability Tables 1a and 1b suggest that indicators of U.S. inflation have significant predictive power for the nominal price of oil. This result is in striking contrast to the pre-1973 period. As shown in Hamilton (1983) using quarterly data and in
- Exact
- Gillman and Nakov (2009)
- Suffix
- using monthly data, there is no significant Granger causality from U.S. inflation to the percent change in the nominal price of oil in the 1950s and 1960s. This difference in results is suggestive of a structural break in late 1973 in the predictive relationship between the price of oil and the U.

- In-text reference with the coordinate start=19882
- 43
- Goldberg, P. (1998), “The Effects of the Corporate Average Fuel Economy Standards in the U.S.,” Journal of Industrial Economics, 46, 1-33.

Total in-text references: 1- In-text reference with the coordinate start=6307
- Prefix
- Section 4 studies the extent to which the nominal price of oil and the real price of oil are predictable based on macroeconomic aggregates. We document strong evidence of predictability 1 See, e.g., Kahn (1986), Davis and Kilian (2010). 2 See, e.g.,
- Exact
- Goldberg (1998), Allcott and Wozny (2010), Busse, Knittel and Zettelmeyer (2010), Kellogg (2010).
- Suffix
- in population. Predictability in population, however, need not translate into out-of-sample forecastability. The latter question is the main focus of sections 5 through 8. In sections 5, 6 and 7, we compare a wide range of out-of-sample forecasting methods for the nominal price of oil.

- In-text reference with the coordinate start=6307
- 45
- Gramlich, E.M. (2004), ”Oil Shocks and Monetary Policy,” Annual Economic Luncheon, Federal Reserve Bank of Kansas City, Kansas City, Missouri.

Total in-text references: 1- In-text reference with the coordinate start=86498
- Prefix
- Notwithstanding the low liquidity of oil futures markets at such long horizons, documented in Alquist and Kilian (2010), it is precisely these long horizons that many policymakers focus on. For example, Greenspan (2004a) explicitly referred to the 6-year oil futures contract in assessing effective long-term supply prices. For similar statements also see
- Exact
- Greenspan (2004b), Gramlich (2004) and Bernanke (2004).
- Suffix
- In this section we focus on forecasting the nominal price of oil at horizons up to seven years. It can be shown that the daily data are too sparse at horizons beyond one year to allow the construction of time series of end-of-month observations for oil futures prices.

- In-text reference with the coordinate start=86498
- 46
- Green, E.J., and R.H. Porter (1984), “Noncooperative Collusion under Imperfect Price Information,” Econometrica, 52, 87-100.

Total in-text references: 1- In-text reference with the coordinate start=21893
- Prefix
- Moreover, as observed by Barsky and Kilian (2002), economic theory predicts that the strength of the oil cartel itself (measured by the extent to which individual cartel members choose to deviate from cartel guidelines) will be positively related to the state of the global business cycle (see
- Exact
- Green and Porter 1984).
- Suffix
- Thus, both nominal and real oil prices must be considered endogenous with respect to the global economy, unless proven otherwise. A third and distinct argument has been that consumers of refined oil products choose to respond to changes in the nominal price of oil rather than the real price of oil, perhaps because the nominal price of oil is more visible.

- In-text reference with the coordinate start=21893
- 47
- Greenspan, A. (2004a), “Energy” Remarks by Chairman Alan Greenspan Before the Center for Strategic & International Studies, Washington, D.C. http://www.federalreserve.gov/boarddocs/speeches/2004/20040427/default.htm

Total in-text references: 1- In-text reference with the coordinate start=86344
- Prefix
- Notwithstanding the low liquidity of oil futures markets at such long horizons, documented in Alquist and Kilian (2010), it is precisely these long horizons that many policymakers focus on. For example,
- Exact
- Greenspan (2004a)
- Suffix
- explicitly referred to the 6-year oil futures contract in assessing effective long-term supply prices. For similar statements also see Greenspan (2004b), Gramlich (2004) and Bernanke (2004). In this section we focus on forecasting the nominal price of oil at horizons up to seven years.

- In-text reference with the coordinate start=86344
- 48
- Greenspan, A. (2004b), “Oil,” Speech presented at the National Italian American Foundation, Washington, DC. htttp://www.federalreserve.gov/boarddocs/speeches/2004/200410152/default.htm

Total in-text references: 1- In-text reference with the coordinate start=86498
- Prefix
- Notwithstanding the low liquidity of oil futures markets at such long horizons, documented in Alquist and Kilian (2010), it is precisely these long horizons that many policymakers focus on. For example, Greenspan (2004a) explicitly referred to the 6-year oil futures contract in assessing effective long-term supply prices. For similar statements also see
- Exact
- Greenspan (2004b), Gramlich (2004) and Bernanke (2004).
- Suffix
- In this section we focus on forecasting the nominal price of oil at horizons up to seven years. It can be shown that the daily data are too sparse at horizons beyond one year to allow the construction of time series of end-of-month observations for oil futures prices.

- In-text reference with the coordinate start=86498
- 49
- Hamilton, J.D. (1983), “Oil and the Macroeconomy Since World War II,” Journal of Political Economy, 91, 228-248.

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- The WTI data until 1973 tend to exhibit a pattern resembling a step-function. The price remains constant for extended periods, followed by discrete adjustments. The U.S. wholesale price of oil for 1948-1972 used in
- Exact
- Hamilton (1983)
- Suffix
- is numerically identical with this WTI series. As discussed in Hamilton (1983, 1985) the discrete pattern of crude oil price changes during this period is explained by the specific regulatory structure of the oil industry during 1948-72.

- In-text reference with the coordinate start=13795
- Prefix
- The price remains constant for extended periods, followed by discrete adjustments. The U.S. wholesale price of oil for 1948-1972 used in Hamilton (1983) is numerically identical with this WTI series. As discussed in
- Exact
- Hamilton (1983, 1985)
- Suffix
- the discrete pattern of crude oil price changes during this period is explained by the specific regulatory structure of the oil industry during 1948-72. Each month the Texas Railroad Commission and other U.

- In-text reference with the coordinate start=14514
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- As a result, much of the cyclically endogenous component of oil demand was reflected in shifts in quantities rather than prices. The commission was generally unable or unwilling to accommodate sudden disruptions in oil production, preferring instead to exploit these events to implement sometimes dramatic price increases
- Exact
- (Hamilton 1983,
- Suffix
- p. 230). Whereas the WTI price is a good proxy for the U.S. price for oil during 1948-72, when the U.S. was largely self-sufficient in oil, it becomes less representative after 1973, when the share of U.

- In-text reference with the coordinate start=27299
- Prefix
- The Pre-1973 Evidence Granger causality from macroeconomic aggregates to the price of oil has received attention in part because Granger non-causality is one of the testable implications of strict exogeneity. The notion that the percent change in the nominal price of oil may be considered exogenous with respect to the U.S. economy was bolstered by evidence in
- Exact
- Hamilton (1983),
- Suffix
- who observed that there is no apparent Granger causality from U.S. domestic macroeconomic aggregates to the percent change in the nominal price of oil during 1948-1972. Of course, the absence of Granger causality is merely a necessary condition for strict exogeneity.

- In-text reference with the coordinate start=28410
- Prefix
- under this institutional regime appear to be associated with exogenous political events in the Middle East, allowing us to treat the resulting price spikes as exogenous with respect to the U.S. economy. For a more nuanced view of these historical episodes see Kilian (2008b; 2009a,b; 2010). Even if we accept Hamilton’s interpretation of the pre-1973 period, the institutional conditions that
- Exact
- Hamilton (1983)
- Suffix
- appeals to ceased to exist in the early 1970s, and Hamilton’s results for the 1948-1972 period are mainly of historical interest. The real question for our purposes is to what extent there is evidence that oil prices can be predicted from macroeconomic aggregates in the post-1973 period. 4.1.2.

- In-text reference with the coordinate start=38606
- Prefix
- Reconciling the Pre- and Post-1973 Evidence on Predictability Tables 1a and 1b suggest that indicators of U.S. inflation have significant predictive power for the nominal price of oil. This result is in striking contrast to the pre-1973 period. As shown in
- Exact
- Hamilton (1983)
- Suffix
- using quarterly data and in Gillman and Nakov (2009) using monthly data, there is no significant Granger causality from U.S. inflation to the percent change in the nominal price of oil in the 1950s and 1960s.

- In-text reference with the coordinate start=13716
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- Hamilton, J.D. (1985), “Historical Causes of Postwar Oil Shocks and Recessions,” Energy Journal, 6, 97–116.

Total in-text references: 1- In-text reference with the coordinate start=13795
- Prefix
- The price remains constant for extended periods, followed by discrete adjustments. The U.S. wholesale price of oil for 1948-1972 used in Hamilton (1983) is numerically identical with this WTI series. As discussed in
- Exact
- Hamilton (1983, 1985)
- Suffix
- the discrete pattern of crude oil price changes during this period is explained by the specific regulatory structure of the oil industry during 1948-72. Each month the Texas Railroad Commission and other U.

- In-text reference with the coordinate start=13795
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- Hamilton, J.D. (1994), Time Series Analysis, Princeton, NJ: Princeton University Press.

Total in-text references: 1- In-text reference with the coordinate start=31009
- Prefix
- Table 1a investigates the evidence of Granger causality from selected nominal U.S. macroeconomic variables to the nominal price of oil. All results are based on pairwise vector autoregressions. The lag order is fixed at 12. Similar results would have been obtained 7
- Exact
- Hamilton (1994,
- Suffix
- p. 306) illustrates this point in the context of a model of stock prices and expected dividends. with 24 lags. We consider four alternative nominal oil price series. The evaluation period is alternatively 1973.1-2009.12 or 1975.1-2009.12.8 It is not clear a priori which oil price series is best suited for finding predictability.

- In-text reference with the coordinate start=31009
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- Hamilton, J. D. (1996). “This is What Happened to the Oil Price–Macroeconomy Relationship,” Journal of Monetary Economics, 38, 215–220.

Total in-text references: 3- In-text reference with the coordinate start=23191
- Prefix
- There is evidence from in-sample fitting exercises, however, of a predictive relationship between suitable nonlinear transformations of the nominal price of oil and U.S. real output, in particular. The most successful of these transformations is the net oil price increase measure of
- Exact
- Hamilton (1996, 2003).
- Suffix
- Let ts denote the nominal price of oil in logs and the difference operator. Then the net oil price increase is defined as: ,* max 0,, net sssttt where * st is the highest oil price in the preceding 12 months or, alternatively, the preceding 36 months.

- In-text reference with the coordinate start=143307
- Prefix
- That adjustment as well serves to remove a nonlinearity in the transmission process. Both the nonlinearity postulated in Edelstein and Kilian (2009) and that postulated in Ramey and Vine (2010) is incompatible with the specific nonlinearity embodied in the models of
- Exact
- Mork (1989) and Hamilton (1996, 2003).
- Suffix
- In fact, the aforementioned papers rely on linear regressions after adjusting the energy price data. augmented by other variables. Recently, Kilian and Vigfusson (2010a) have shown that impulse response estimates from VAR models involving censored oil price variables are inconsistent even when equation (18) is correctly specified.

- In-text reference with the coordinate start=152631
- Prefix
- of lagged percent changes in the nominal price of oil (accounting for 4 percentage points by itself) are mainly responsible for the additional gain in accuracy; the imposition of exogeneity plays no role. Accuracy gains at slightly shorter or longer horizons are closer to 10%. Second, neither the percent increase model based on Mork (1989) nor the one-year net increase model motivated by
- Exact
- Hamilton (1996)
- Suffix
- is more accurate than the AR(4) benchmark at the one-quarter horizon. This is true regardless of whether the price of oil is specified in nominal or real terms and regardless of what additional restrictions we impose.

- In-text reference with the coordinate start=23191
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- Hamilton, J. D. (2003) “What is an Oil Shock?” Journal of Econometrics, 113, 363–398.

Total in-text references: 10- In-text reference with the coordinate start=23191
- Prefix
- There is evidence from in-sample fitting exercises, however, of a predictive relationship between suitable nonlinear transformations of the nominal price of oil and U.S. real output, in particular. The most successful of these transformations is the net oil price increase measure of
- Exact
- Hamilton (1996, 2003).
- Suffix
- Let ts denote the nominal price of oil in logs and the difference operator. Then the net oil price increase is defined as: ,* max 0,, net sssttt where * st is the highest oil price in the preceding 12 months or, alternatively, the preceding 36 months.

- In-text reference with the coordinate start=25008
- Prefix
- Hamilton (2010), for example, interprets this specification as capturing nonlinear changes in consumer sentiment in response to nominal oil price increases.6 As with other oil price specifications there is reason to expect lagged feedback from global macroeconomic aggregates to the net oil price increase. Whereas
- Exact
- Hamilton (2003)
- Suffix
- made the case that net oil price increases in the 1970s, 1980s and 1990s were capturing exogenous events in the Middle East, Hamilton (2009) concedes that the net oil price increase of 2003-08 was driven in large part by a surge in the demand for oil.

- In-text reference with the coordinate start=25719
- Prefix
- evidence based on structural VAR models that in fact most net oil price increases have contained a large demand component driven by global macroeconomic conditions, even 6 Interestingly, the behavioral rationale for the net oil price increase measure applies equally to the nominal price of oil and the real price of oil. Although
- Exact
- Hamilton (2003)
- Suffix
- applied this transformation to the nominal price of oil, several other studies have recently explored models that apply the same transformation to the real price of oil (see, e.g., Kilian and Vigfusson 2010a; Herrera, Lagalo and Wada 2010). prior to 2003.

- In-text reference with the coordinate start=136887
- Prefix
- We determined the lag order of this benchmark model based on a forecast accuracy comparison involving all combinations of horizons 1,..., 8hand lag orders 1,..., 24 .pThe AR(4) model for real GDP growth proved to have the lowest MSPE or about the same MSPE as the most accurate model at all horizons. The same AR(4) benchmark model has also been used by
- Exact
- Hamilton (2003) and
- Suffix
- others, facilitating comparisons with existing results in the literature. We compare the benchmark model with two alternative models. One model is the unrestricted VAR(p) model obtained with 1112 2122 ()() ().

- In-text reference with the coordinate start=141891
- Prefix
- Of particular concern is the possibility that nonlinear dynamic regression models may generate more accurate out-of-sample forecasts of cumulative real GDP growth. 11.2. Nonlinear Dynamic Models In this regard,
- Exact
- Hamilton (2003)
- Suffix
- suggested that the predictive relationship between oil prices and U.S. real GDP is nonlinear in that (1) oil price increases matter only to the extent that they exceed the maximum oil price in recent years and that (2) oil price decreases do not matter at all.

- In-text reference with the coordinate start=143307
- Prefix
- That adjustment as well serves to remove a nonlinearity in the transmission process. Both the nonlinearity postulated in Edelstein and Kilian (2009) and that postulated in Ramey and Vine (2010) is incompatible with the specific nonlinearity embodied in the models of
- Exact
- Mork (1989) and Hamilton (1996, 2003).
- Suffix
- In fact, the aforementioned papers rely on linear regressions after adjusting the energy price data. augmented by other variables. Recently, Kilian and Vigfusson (2010a) have shown that impulse response estimates from VAR models involving censored oil price variables are inconsistent even when equation (18) is correctly specified.

- In-text reference with the coordinate start=145737
- Prefix
- First, even granting the presence of asymmetries in the predictive model, one question is whether the predictive model should be specified as 44 ,,3 11 netyr titiitit ii yysu , (18) as in
- Exact
- Hamilton (2003),
- Suffix
- or rather as: 444 ,,3 111 netyr titiitiitit iii yyssu (19) as in Balke, Brown and Yücel (2002) or Herrera, Lagalo and Wada (2010), for example.

- In-text reference with the coordinate start=148790
- Prefix
- For completeness, we also include results for the percent increase specification proposed in Mork (1989), the forecasting performance of which has not been investigated to date. We consider nonlinear models based on the real price of oil as in Kilian and Vigfusson and nonlinear models based on the nominal price of oil as in
- Exact
- Hamilton (2003).
- Suffix
- The unrestricted multivariate nonlinear forecasting model takes the form 44 111,12,1, 11 44 4 221,22,2, 11 1 titiitit ii titiitiitit ii i rBrBye yBrByr e (20) where ,,3,,1,,,netyrnetyrttttrrrr(0)tttrrIr as in Mork (1989), and I(•) denotes the ind

- In-text reference with the coordinate start=150038
- Prefix
- ii i rBre yBrByr e (21) and 4 111,1, 1 44 4 221,22,2, 11 1 titit i titiitiitit ii i sBse yBsBys e (21) Alternatively, we may restrict the feedback from lagged percent changes in the price of oil, as suggested by
- Exact
- Hamilton (2003).
- Suffix
- After imposing21,0,iBi the baseline nonlinear forecasting model reduces to: 44 111,12,1, 11 44 222,2, 11 titiitit ii titiitit ii rBrBye yByre (22) and 44 111,12,1, 11 44 222,2, 11 titiitit ii titiitit ii

- In-text reference with the coordinate start=193523
- Prefix
- For example, we found no evidence that the nominal PPI three-year net increase model is more accurate than linear models for real GDP growth at the one-quarter horizon. A multivariate generalization of the model proposed by
- Exact
- Hamilton (2003, 2010)
- Suffix
- tended to provide MSPE gains of up to 12% relative to the AR(4) benchmark model at longer horizons. Even more accurate results were obtained with some alternative oil price series. All these forecasting successes, however, were driven entirely by the 2008/09 recession.

- In-text reference with the coordinate start=23191
- 54
- Hamilton, J.D. (2009), “Causes and Consequences of the Oil Shock of 2007-08,” Brookings Papers on Economic Activity, 1, Spring, 215-261.

Total in-text references: 3- In-text reference with the coordinate start=4251
- Prefix
- For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. For example,
- Exact
- Hamilton (2009),
- Suffix
- building on the analysis in Edelstein and Kilian (2009), provides evidence that the recession of late 2008 was amplified and preceded by an economic slowdown in the automobile industry and a deterioration in consumer sentiment.

- In-text reference with the coordinate start=25149
- Prefix
- capturing nonlinear changes in consumer sentiment in response to nominal oil price increases.6 As with other oil price specifications there is reason to expect lagged feedback from global macroeconomic aggregates to the net oil price increase. Whereas Hamilton (2003) made the case that net oil price increases in the 1970s, 1980s and 1990s were capturing exogenous events in the Middle East,
- Exact
- Hamilton (2009)
- Suffix
- concedes that the net oil price increase of 2003-08 was driven in large part by a surge in the demand for oil. Kilian (2009a,b; 2010), on the other hand, provides evidence based on structural VAR models that in fact most net oil price increases have contained a large demand component driven by global macroeconomic conditions, even 6

- In-text reference with the coordinate start=141297
- Prefix
- This suggests that we replace the percent change in the real price of oil in the linear VAR model by the percent change in the real price of oil weighted by the time-varying share of oil in domestic expenditures, building on the analysis in
- Exact
- Edelstein and Kilian (2009). Hamilton (2009)
- Suffix
- reported some success in employing a similar strategy.31 Another source of time variation may be changes in the composition of the underlying oil demand and oil supply shocks, as discussed in Kilian (2009).

- In-text reference with the coordinate start=4251
- 55
- Hamilton, J.D. (2010), “Nonlinearities and the Macroeconomic Effects of Oil Prices,” forthcoming: Macroeconomic Dynamics.

Total in-text references: 9- In-text reference with the coordinate start=12309
- Prefix
- The net oil price increase is a censored predictor that assigns zero weight to net oil price decreases. There is little evidence that this type of asymmetry is reflected in the responses of U.S. real GDP to innovations in the real price of oil, as documented in Kilian and Vigfusson (2010a,b), but
- Exact
- Hamilton (2010)
- Suffix
- suggests that the net oil price increase specification is best thought of as a parsimonious forecasting device. We provide a comprehensive analysis of this conjecture. Point forecasts of the price of oil are important, but they fail to convey the large uncertainty associated with oil price forecasts.

- In-text reference with the coordinate start=24693
- Prefix
- Nevertheless, Hamilton’s nominal net oil price increase variable has become one of the leading specifications in the literature on predictive relationships between the price of oil and the U.S. economy.
- Exact
- Hamilton (2010),
- Suffix
- for example, interprets this specification as capturing nonlinear changes in consumer sentiment in response to nominal oil price increases.6 As with other oil price specifications there is reason to expect lagged feedback from global macroeconomic aggregates to the net oil price increase.

- In-text reference with the coordinate start=144803
- Prefix
- Kilian and Vigfusson proposed a direct test of the latter hypothesis and showed empirically that there is no statistically significant evidence of asymmetry in the response functions for U.S. real GDP.
- Exact
- Hamilton (2010)
- Suffix
- agrees with Kilian and Vigfusson on the lack of validity of impulse response analysis from censored oil price VAR models, but suggests that nonlinear predictive models such as model (18) may still be useful for out-of-sample forecasting.

- In-text reference with the coordinate start=145333
- Prefix
- We consider both one-quarter-ahead forecasts of real GDP growth and forecasts of the cumulative real GDP growth rate several quarters ahead. The latter forecasts require a generalization of the single-equation forecasting approach proposed by
- Exact
- Hamilton (2010).
- Suffix
- In implementing this approach, there are several potentially important modeling choices to be made. First, even granting the presence of asymmetries in the predictive model, one question is whether the predictive model should be specified as 44 ,,3 11 netyr titiitit ii yysu , (18) as in

- In-text reference with the coordinate start=146862
- Prefix
- This motivation for the use of model (18) is new in that heretofore the focus in the literature – including Hamilton’s own work – has been on establishing nonlinear predictability in population rather than out-of-sample.
- Exact
- Hamilton (2010)
- Suffix
- is, of course, correct that there is a tradeoff between estimation variance and bias. Indeed, in many other contexts parsimony has been shown to help reduce the out-of-sample MSPE, but no systematic evidence has been presented to make this case for this model.

- In-text reference with the coordinate start=147627
- Prefix
- A second point of contention is whether nonlinear forecasting models should be specified in terms of the nominal price of oil or the real price of oil. For linear models, a strong economic case can be made for using the real price of oil. For nonlinear models, the situation is less clear, as noted by
- Exact
- Hamilton (2010).
- Suffix
- Because the argument for using net oil price increases is behavioral, one specification appears as reasonable as the other. Below we therefore will consider models specified in real as well as in nominal oil prices.

- In-text reference with the coordinate start=158303
- Prefix
- The first two columns of Table 20 focus on the evaluation period 1990.Q1-2010.Q2. Column (1) shows that, for eight of ten model specifications, the one-quarter ahead nonlinear forecasting model proposed by
- Exact
- Hamilton (2010)
- Suffix
- fails to outperform the AR(4) benchmark model for real GDP. Only for the real refiners’ acquisition cost for imported crude oil and for the nominal WTI specification are there any gains in forecast accuracy.

- In-text reference with the coordinate start=158583
- Prefix
- Only for the real refiners’ acquisition cost for imported crude oil and for the nominal WTI specification are there any gains in forecast accuracy. In particular, the nominal PPI specification favored by
- Exact
- Hamilton (2010)
- Suffix
- on the basis of in-sample diagnostics is less accurate than the AR benchmark model. Much more favorable results are obtained at the one-year horizon in column (2) of Table 20. All but one nonlinear forecasting model yields reductions in the MSPE, although the extent of these reductions greatly differs across models and can range from negligible to substantial.

- In-text reference with the coordinate start=193523
- Prefix
- For example, we found no evidence that the nominal PPI three-year net increase model is more accurate than linear models for real GDP growth at the one-quarter horizon. A multivariate generalization of the model proposed by
- Exact
- Hamilton (2003, 2010)
- Suffix
- tended to provide MSPE gains of up to 12% relative to the AR(4) benchmark model at longer horizons. Even more accurate results were obtained with some alternative oil price series. All these forecasting successes, however, were driven entirely by the 2008/09 recession.

- In-text reference with the coordinate start=12309
- 56
- Hamilton, J.D., and A.M. Herrera (2004), “Oil Shocks and Aggregate Economic Behavior: The Role of Monetary Policy,” Journal of Money, Credit and Banking, 36, 265-286.

Total in-text references: 1- In-text reference with the coordinate start=53090
- Prefix
- It also highlights a fourth issue. There is evidence that allowing for two years worth of lags rather than one year often strengthens the significance of the rejections. This finding mirrors the point made in
- Exact
- Hamilton and Herrera (2004)
- Suffix
- that it is essential to allow for a rich lag structure in studying the dynamic relationship between the economy and the price of oil. Although none of the proxies for global fluctuations in demand is without limitations, we conclude that there is a robust pattern of Granger causality, as we correct for problems of model misspecification and of data mismeasurement that undermine the power of th

- In-text reference with the coordinate start=53090
- 57
- Hendry, D. (2006), “Robustifying Forecasts from Equilibrium-Correction Systems,” Journal of Econometrics, 135, 399-426

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- Prefix
- Parsimonious Econometric Forecasts One example of parsimonious econometric forecasting models is the random walk model without drift introduced earlier. An alternative is the double-differenced forecasting model proposed in
- Exact
- Hendry (2006).
- Suffix
- Hendry observed that when time series are subject to infrequent trend changes, the no-change forecast may be improved upon by extrapolating today’s oil price at the most recent growth rate: |ˆ1 h SsthtttS 1, 3, 6, 9, 1 2h (7) where ts denotes the percent growth rate between 1t and .t In other words, we apply the nochange forecast to the growth rate rather than the

- In-text reference with the coordinate start=68076
- 58
- Herrera, A.M., Lagalo, L.G., and T. Wada (2010), “Oil Price Shocks and Industrial Production: Is the Relationship Linear?” forthcoming: Macroeconomic Dynamics.

Total in-text references: 2- In-text reference with the coordinate start=25916
- Prefix
- Although Hamilton (2003) applied this transformation to the nominal price of oil, several other studies have recently explored models that apply the same transformation to the real price of oil (see, e.g.,
- Exact
- Kilian and Vigfusson 2010a; Herrera, Lagalo and Wada 2010).
- Suffix
- prior to 2003. This finding is also consistent with the empirical results in Baumeister and Peersman (2010). For now we set aside all nonlinear transformations of the price of oil and focus on linear forecasting models for the nominal price of oil and for the real price of oil.

- In-text reference with the coordinate start=145949
- Prefix
- be specified as 44 ,,3 11 netyr titiitit ii yysu , (18) as in Hamilton (2003), or rather as: 444 ,,3 111 netyr titiitiitit iii yyssu (19) as in Balke, Brown and Yücel (2002) or
- Exact
- Herrera, Lagalo and Wada (2010),
- Suffix
- for example. The latter specification encompasses the linear reduced-form model as a special case. Kilian and Vigfusson prove that dropping the lagged percent changes from model (19) will cause an inconsistency of the OLS estimates, except in the theoretically implausible case that there is no lagged feedback from percent changes in the price of oil to real GDP.

- In-text reference with the coordinate start=25916
- 59
- Holthausen, D.M. (1981), “A Risk-Return Model with Risk and Return Measured in Deviations from Target Return,” American Economic Review, 71, 182–88.

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- Prefix
- In particular, if and only if the loss function is quadratic and symmetric about zero, the variance of the price of oil about zero provides an adequate summary statistic for the risk in oil price forecasts. Even that target variance, however, is distinct 38 Measures of risk of this type were first introduced by
- Exact
- Fishburn (1977), Holthausen (1981),
- Suffix
- Artzner, Delbaen, Eber and Heath (1999), and Basak and Shapiro (2001) in the context of portfolio risk management and have become a standard tool in recent years (see, e.g., Engle and Brownlees 2010).

- In-text reference with the coordinate start=176748
- 60
- Hotelling, H. (1931), “The Economics of Exhaustible Resources,” Journal of Political Economy, 39, 137-175.

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- Prefix
- There is no evidence of significant forecast accuracy gains at shorter horizons, and at the long horizons of interest to policymakers, oil futures prices are clearly inferior to the no-change forecast. Similarly, forecasting models based on the dollar exchange rates of major commodity exporters, models based on the
- Exact
- Hotelling (1931), and
- Suffix
- a variety of simple time series regression models are not successful at significantly lowering the MSPE at short horizons. There is evidence, however, that recent percent changes in the nominal price of industrial raw materials other than oil can be used to substantially and significantly reduce the MSPE of the no-change forecast of the nominal price of oil at horizons of 1 and 3 months.

- In-text reference with the coordinate start=67599
- Prefix
- While economists have used survey data extensively in measuring the risk premium embedded in foreign exchange futures, this approach has not been applied to oil futures, with the exception of recent work by Wu and McCallum (2005). Yet another approach is to exploit the implication of the
- Exact
- Hotelling (1931)
- Suffix
- model that the price of oil should grow at the rate of interest. Finally, we also consider forecasting models that adjust the no-change forecast for inflation expectations and for recent percent changes in other nominal prices. 5.2.1.

- In-text reference with the coordinate start=7724
- 61
- Inoue, A., and L. Kilian (2004a), “In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?” Econometric Reviews, 23, 371-402.

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- Prefix
- Granger Causality Tests Much of the existing work on predicting the price of oil has focused on testing for the existence of a predictive relationship from macroeconomic aggregates to the price of oil. The existence of predictability in population is a necessary precondition for out-of-sample forecastability (see
- Exact
- Inoue and Kilian 2004a).
- Suffix
- Within the linear VAR framework the absence of predictability from one variable to another in population may be tested using Granger non-causality tests. 4.1. Nominal Oil Price Predictability 4.1.1.

- In-text reference with the coordinate start=53947
- Prefix
- by evidence in Kilian and Hicks (2010) based on distributed lag models that revisions to professional real GDP growth forecasts have significant predictive power for the real price of oil during 2000.11-2008.12 after weighting each country’s forecast revision by its PPP-GDP share. Predictability in population, of course, does not necessarily imply out-of-sample forecastability (see
- Exact
- Inoue and Kilian 2004a).
- Suffix
- The next two sections therefore examine alternative approaches to forecasting the nominal and the real price of oil outof-sample. 5. Short-Horizon Forecasts of the Nominal Price of Oil The most common approach to forecasting the nominal price of oil is to treat the price of the oil 16 This index is constructed from ocean shipping

- In-text reference with the coordinate start=64607
- Prefix
- It should be noted that commonly used tests of equal predictive accuracy for nested models (including the tests we rely on in this chapter) by construction are tests of the null of no predictability in population rather than tests of equal outof-sample MSPEs (see, e.g.,
- Exact
- Inoue and Kilian 2004a,b; Clark and McCracken 2010).
- Suffix
- This means that these tests will reject the null of equal predictive accuracy more often than they should under the null, suggesting caution in interpreting test results that are only marginally statistically significant.

- In-text reference with the coordinate start=113654
- Prefix
- The reason for this counterintuitive result is that, as discussed earlier, standard tests of equal predictive accuracy do not test the null of equal out-of-sample MSPEs, but actually test the null of no predictability in population – much like the Granger causality tests we applied earlier – as pointed out by
- Exact
- Inoue and Kilian (2004a).
- Suffix
- This point is readily apparent from the underlying proofs of asymptotic validity as well as the way in which critical values are simulated. The distinction between population predictability and out-of-sample predictability does not matter asymptotically under fixed parameter asymptotics, but fixed parameter asymptotics typically provide a poor approximation to the finite-sample accuracy of f

- In-text reference with the coordinate start=26714
- 62
- Inoue, A., and L. Kilian (2004b), “Bagging Time Series Models,” CEPR Discussion Paper No. 4333.

Total in-text references: 2- In-text reference with the coordinate start=64607
- Prefix
- It should be noted that commonly used tests of equal predictive accuracy for nested models (including the tests we rely on in this chapter) by construction are tests of the null of no predictability in population rather than tests of equal outof-sample MSPEs (see, e.g.,
- Exact
- Inoue and Kilian 2004a,b; Clark and McCracken 2010).
- Suffix
- This means that these tests will reject the null of equal predictive accuracy more often than they should under the null, suggesting caution in interpreting test results that are only marginally statistically significant.

- In-text reference with the coordinate start=104745
- Prefix
- The local-to-zero asymptotic approximation of predictive models suggests that using the no-change forecast may lower the asymptotic MSPE even relative to the correctly specified non-random walk model, provided the local drift parameter governing the predictive relationship is close enough to zero (see, e.g.,
- Exact
- Inoue and Kilian (2004b), Clark and McCracken 2010).
- Suffix
- 26 The refiners’ acquisition cost was extrapolated back to 1973.2 as in Barsky and Kilian (2002). selection (see Inoue and Kilian 2006; Marcellino, Stock and Watson 2006). We search over p0,...,12 .

- In-text reference with the coordinate start=64607
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- Inoue, A., and L. Kilian (2006), “On the Selection of Forecasting Models,” Journal of Econometrics, 130, 273-306.

Total in-text references: 2- In-text reference with the coordinate start=104916
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- no-change forecast may lower the asymptotic MSPE even relative to the correctly specified non-random walk model, provided the local drift parameter governing the predictive relationship is close enough to zero (see, e.g., Inoue and Kilian (2004b), Clark and McCracken 2010). 26 The refiners’ acquisition cost was extrapolated back to 1973.2 as in Barsky and Kilian (2002). selection (see
- Exact
- Inoue and Kilian 2006; Marcellino, Stock and Watson 2006).
- Suffix
- We search over p0,...,12 . The forecast accuracy results are robust to allowing for a larger upper bound. There are no theoretical results in the forecasting literature on how to assess the null of equal predictive accuracy when comparing iterated AR or ARMA forecasts to the no-change forecast.

- In-text reference with the coordinate start=110555
- Prefix
- It has been shown that the presence of structural breaks at unknown points in the future invalidates the use of forecasting model rankings obtained in forecast accuracy comparisons whether one uses rolling or recursive regression forecasts (see
- Exact
- Inoue and Kilian 2006).
- Suffix
- 29 It also outperforms the random walk model with drift in both of these dimensions, whether the drift is estimated recursively or as the average growth rate over the most recent h months. These results are not shown to conserve space. statistically significant reductions in the MSPE.

- In-text reference with the coordinate start=104916
- 64
- Isserlis, L. (1938), “Tramp Shipping Cargoes and Freights,” Journal of the Royal Statistical Society, 101(1), 53-134.

Total in-text references: 1- In-text reference with the coordinate start=54505
- Prefix
- -Horizon Forecasts of the Nominal Price of Oil The most common approach to forecasting the nominal price of oil is to treat the price of the oil 16 This index is constructed from ocean shipping freight rates. The idea of using fluctuations in shipping freight rates as indicators of shifts in the global real activity dates back to
- Exact
- Isserlis (1938) and Tinbergen (1959).
- Suffix
- The panel of monthly freight-rate data underlying the global real activity index was collected manually from Drewry’s Shipping Monthly using various issues since 1970. The data set is restricted to dry cargo rates.

- In-text reference with the coordinate start=54505
- 67
- Kahn, J.A. (1986), “Gasoline Prices and the Used Automobile Market: A Rational Expectations Asset Price Approach,” Quarterly Journal of Economics, 101, 323-340.

Total in-text references: 2- In-text reference with the coordinate start=6256
- Prefix
- Section 4 studies the extent to which the nominal price of oil and the real price of oil are predictable based on macroeconomic aggregates. We document strong evidence of predictability 1 See, e.g.,
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- Kahn (1986), Davis and Kilian (2010).
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- 2 See, e.g., Goldberg (1998), Allcott and Wozny (2010), Busse, Knittel and Zettelmeyer (2010), Kellogg (2010). in population. Predictability in population, however, need not translate into out-of-sample forecastability.

- In-text reference with the coordinate start=93372
- Prefix
- A variety of modeling strategies has been explored, often with widely different results. Candidates include ARIMA models, no-change forecasts, oil futures prices and gasoline futures prices (see, e.g.,
- Exact
- Kahn 1986; Davis and Kilian 2010; Allcott and Wozny 2010).
- Suffix
- The issue is not only one of finding a forecasting method that achieves the smallest possible out-of-sample forecast error, but of understanding how consumers form their price expectations. An obvious concern is that actual consumer expectations may differ from the predictions generated by the forecasting methods considered so far.

- In-text reference with the coordinate start=6256
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- Kellogg, R. (2010), “The Effect of Uncertainty on Investment: Evidence from Texas Oil Drilling,” mimeo, Department of Economics, University of Michigan.

Total in-text references: 4- In-text reference with the coordinate start=6307
- Prefix
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- Goldberg (1998), Allcott and Wozny (2010), Busse, Knittel and Zettelmeyer (2010), Kellogg (2010).
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- In-text reference with the coordinate start=168582
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- An analogous argument holds for consumers considering the purchase of energy-intensive durables such as cars. Real options theory implies that, all else equal, an increase in expected volatility will cause marginal investment decisions to be postponed, causing a reduction in investment expenditures.
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- Kellogg (2010)
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- provides evidence that such mechanisms are at work in the Texas oil industry, for example. Unlike in empirical finance, the relevant volatility measure in these models is the volatility of the real price of oil at horizons relevant to purchase and investment decisions, which is typically measured in years or even decades rather than days or months, making standard measures of short-term no

- In-text reference with the coordinate start=171271
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- That might be the case at a threshold of $120 a barrel, for example, at 35 In rare cases, the relevant forecast horizon may be short enough for empirical analysis. For example,
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- Kellogg (2010)
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- makes the case that for the purpose of drilling oil wells in Texas, as opposed to Saudi Arabia, a forecast horizon of only 18 months is adequate. Even at that horizon, however, there are no oil-futures options price data that would allow the construction of implied volatility measures.

- In-text reference with the coordinate start=171573
- Prefix
- For example, Kellogg (2010) makes the case that for the purpose of drilling oil wells in Texas, as opposed to Saudi Arabia, a forecast horizon of only 18 months is adequate. Even at that horizon, however, there are no oil-futures options price data that would allow the construction of implied volatility measures.
- Exact
- Kellogg (2010)
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- therefore converts the one-month volatility to 18-month volatilities based on the term structure of oil futures. That approach relies on the assumption that oil futures prices are reliable predictors of future oil prices. which price major oil producers risk inducing the large-scale use of alternative technologies with adverse consequences for the long-run price of crude oil.36 Thus, the oil

- In-text reference with the coordinate start=6307
- 70
- Kilian, L. (2008a), “The Economic Effects of Energy Price Shocks,” Journal of Economic Literature, 46(4), 871-909.

Total in-text references: 1- In-text reference with the coordinate start=28919
- Prefix
- The real question for our purposes is to what extent there is evidence that oil prices can be predicted from macroeconomic aggregates in the post-1973 period. 4.1.2. The Post-1973 Evidence There is widespread agreement among oil economists that, starting in 1973, nominal oil prices must be considered endogenous with respect to U.S. macroeconomic variables (see
- Exact
- Kilian 2008a).
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- Whether this endogeneity makes the nominal price of oil predictable on the basis of lagged U.S. macroeconomic aggregates depends on whether the price of oil behaves like a typical asset price or not.

- In-text reference with the coordinate start=28919
- 71
- Kilian, L. (2008b), “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?” Review of Economics and Statistics, 90, 216-240.

Total in-text references: 2- In-text reference with the coordinate start=28273
- Prefix
- features of the oil market during this period, discussed in section 2, and on historical evidence that unexpected supply disruptions under this institutional regime appear to be associated with exogenous political events in the Middle East, allowing us to treat the resulting price spikes as exogenous with respect to the U.S. economy. For a more nuanced view of these historical episodes see
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- Kilian (2008b;
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- 2009a,b; 2010). Even if we accept Hamilton’s interpretation of the pre-1973 period, the institutional conditions that Hamilton (1983) appeals to ceased to exist in the early 1970s, and Hamilton’s results for the 1948-1972 period are mainly of historical interest.

- In-text reference with the coordinate start=34461
- Prefix
- Whereas Barsky and Kilian stress the real effects of unanticipated monetary expansions on real domestic output, on the demand for oil and hence on the real price of oil, Gillman and Nakov stress that the relative price of oil must not decline in response to a monetary expansion, necessitating a higher nominal price of oil, consistent with anecdotal evidence on OPEC price decisions (see, e.g.,
- Exact
- Kilian 2008b).
- Suffix
- These two explanations are complementary. from narrow measures of money such as M1 for the refiners’ acquisition cost and the WTI price of oil based on the 1975.2-2009.12 evaluation period. The much weaker evidence for the full WTI series may reflect the stronger effect of regulatory policies on the WTI price during the early 1970s.

- In-text reference with the coordinate start=28273
- 72
- Kilian, L. (2009a), “Not all Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market,” American Economic Review, 99, 1053-1069.

Total in-text references: 4- In-text reference with the coordinate start=25276
- Prefix
- Whereas Hamilton (2003) made the case that net oil price increases in the 1970s, 1980s and 1990s were capturing exogenous events in the Middle East, Hamilton (2009) concedes that the net oil price increase of 2003-08 was driven in large part by a surge in the demand for oil.
- Exact
- Kilian (2009a,b;
- Suffix
- 2010), on the other hand, provides evidence based on structural VAR models that in fact most net oil price increases have contained a large demand component driven by global macroeconomic conditions, even 6 Interestingly, the behavioral rationale for the net oil price increase measure applies equally to the nominal price of oil and t

- In-text reference with the coordinate start=46985
- Prefix
- This possibility is more than a theoretical curiosity in our context. Recent models of the determination of the real price of oil after 1973 have stressed that this price is determined in global markets (see, e.g.,
- Exact
- Kilian 2009a; Kilian and Murphy 2010).
- Suffix
- In particular, the demand for oil depends not merely on U.S. demand, but on global demand. The bivariate model for the real price of oil and U.S. real GDP by construction omits fluctuations in real GDP in the rest of the world.

- In-text reference with the coordinate start=52424
- Prefix
- Even OECD+6 industrial production, however, is an imperfect proxy for business-cycle driven fluctuations in the global demand for industrial commodities such as crude oil. One alternative is the index of global real activity recently proposed in
- Exact
- Kilian (2009a).
- Suffix
- This index does not rely on any country weights and has truly global coverage. It has been constructed with the explicit purpose of measuring fluctuations in the broad-based demand for industrial commodities associated with the global business cycle. 16 As expected, the last row of Table 3 indicates even stronger evidence of Granger causality from this index to the real price of oil, regardle

- In-text reference with the coordinate start=55659
- Prefix
- For this paper, this series has been extended based on the Baltic Exchange Dry Index, which is available from Bloomberg. The latter index, which is commonly discussed in the financial press, is essentially identical to the nominal index in
- Exact
- Kilian (2009a),
- Suffix
- but only available since 1985. futures contract of maturity h as the h-period forecast of the price of oil. 17 In particular, many central banks and the International Monetary Fund (IMF) use the price of NYMEX oil futures as a proxy for the market’s expectation of the spot price of crude oil.

- In-text reference with the coordinate start=25276
- 73
- Kilian, L. (2009b), ““Comment on ‘Causes and Consequences of the Oil Shock of 2007-08’ by James D. Hamilton,” Brookings Papers on Economic Activity, 1, Spring 2009, 267-278.

Total in-text references: 2- In-text reference with the coordinate start=25276
- Prefix
- Whereas Hamilton (2003) made the case that net oil price increases in the 1970s, 1980s and 1990s were capturing exogenous events in the Middle East, Hamilton (2009) concedes that the net oil price increase of 2003-08 was driven in large part by a surge in the demand for oil.
- Exact
- Kilian (2009a,b;
- Suffix
- 2010), on the other hand, provides evidence based on structural VAR models that in fact most net oil price increases have contained a large demand component driven by global macroeconomic conditions, even 6 Interestingly, the behavioral rationale for the net oil price increase measure applies equally to the nominal price of oil and t

- In-text reference with the coordinate start=50659
- Prefix
- For the four shorter series there are three additional rejections for the LT model; the other p-value is not much higher than 0.1. The reduction in p-values compared with U.S. real GDP is dramatic. The fact that there is evidence of predictability only for the linearly detrended series makes sense. As discussed in
- Exact
- Kilian (2009b),
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- the demand for industrial commodities such as crude oil is subject to long swings. Detrending methods such as HP filtering (and even more so first differencing) eliminate much of this low frequency covariation in the data, removing the feature of the data we are interested in testing.

- In-text reference with the coordinate start=25276
- 74
- Kilian, L. (2010), “Explaining Fluctuations in U.S. Gasoline Prices: A Joint Model of the Global Crude Oil Market and the U.S. Retail Gasoline Market,” Energy Journal, 31, 87-104.

Total in-text references: 2- In-text reference with the coordinate start=93033
- Prefix
- Although there can be substantial discrepancies between the evolution of the price of crude oil and the price of gasoline in the short run, long-horizon forecasts of the price of gasoline will track long-horizon forecasts of the price of crude oil (see
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- Kilian 2010).
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- In modeling automobile purchases researchers often need to take a stand on consumers’ expectations of gasoline prices. A variety of modeling strategies has been explored, often with widely different results.

- In-text reference with the coordinate start=102608
- Prefix
- At short horizons, inflation is expected to be at best moderate and it may seem that there is every reason to expect the high forecast accuracy of the random walk model without drift relative to less parsimonious regression models to carry over to the real price of oil (see
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- Kilian 2010).
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- 25 On the other hand, in forecasting the real price of oil we may rely on additional economic structure and on additional predictors that could potentially improve forecast accuracy. Section 8 explores a number of such models.

- In-text reference with the coordinate start=93033
- 75
- Kilian, L., and B. Hicks (2010), “Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008?” mimeo, Department of Economics, University of Michigan.

Total in-text references: 2- In-text reference with the coordinate start=47770
- Prefix
- Only when real GDP fluctuations are highly correlated across countries would we expect U.S. real GDP to be a good proxy for world real GDP. 15 In addition, as the U.S. share in world GDP evolves, by construction so do the predictive correlations underlying Table 2. In this regard,
- Exact
- Kilian and Hicks (2010)
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- have documented dramatic changes in the PPPadjusted share in GDP of the major industrialized economies and of the main emerging economies in recent years that cast further doubt on the U.S. real GDP results in Table 2.

- In-text reference with the coordinate start=53577
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- Although none of the proxies for global fluctuations in demand is without limitations, we conclude that there is a robust pattern of Granger causality, as we correct for problems of model misspecification and of data mismeasurement that undermine the power of the test. This conclusion is further strengthened by evidence in
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- Kilian and Hicks (2010)
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- based on distributed lag models that revisions to professional real GDP growth forecasts have significant predictive power for the real price of oil during 2000.11-2008.12 after weighting each country’s forecast revision by its PPP-GDP share.

- In-text reference with the coordinate start=47770
- 76
- Kilian, L., and S. Manganelli (2007), “Quantifying the Risk of Deflation,” Journal of Money, Credit and Banking, 39, 561-590.

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- Prefix
- 38 Measures of risk of this type were first introduced by Fishburn (1977), Holthausen (1981), Artzner, Delbaen, Eber and Heath (1999), and Basak and Shapiro (2001) in the context of portfolio risk management and have become a standard tool in recent years (see, e.g., Engle and Brownlees 2010). For a general exposition of risk measures and risk management in a different context see
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- Kilian and Manganelli (2007, 2008).
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- from conventionally used measures of oil price volatility, defined as the variance about the sample mean of the predictive distribution. The latter measure under no circumstances can be interpreted as a risk measure because it depends entirely on the predictive distribution of the price of oil and not at all on the user’s preferences.

- In-text reference with the coordinate start=177074
- 77
- Kilian, L., and S. Manganelli (2008), “The Central Banker as a Risk Manager: Estimating the Federal Reserve’s Preferences under Greenspan,” Journal of Money, Credit and Banking, 40, 1103-1129.

Total in-text references: 1- In-text reference with the coordinate start=177074
- Prefix
- 38 Measures of risk of this type were first introduced by Fishburn (1977), Holthausen (1981), Artzner, Delbaen, Eber and Heath (1999), and Basak and Shapiro (2001) in the context of portfolio risk management and have become a standard tool in recent years (see, e.g., Engle and Brownlees 2010). For a general exposition of risk measures and risk management in a different context see
- Exact
- Kilian and Manganelli (2007, 2008).
- Suffix
- from conventionally used measures of oil price volatility, defined as the variance about the sample mean of the predictive distribution. The latter measure under no circumstances can be interpreted as a risk measure because it depends entirely on the predictive distribution of the price of oil and not at all on the user’s preferences.

- In-text reference with the coordinate start=177074
- 78
- Kilian, L., and D. Murphy (2010), “The Role of Inventories and Speculative Trading in the Global Market for Crude Oil,” mimeo, University of Michigan.

Total in-text references: 11- In-text reference with the coordinate start=44356
- Prefix
- Unless the real price of oil is forward looking and already embodies all information about future U.S. real GDP, a reasonable conjecture therefore is that lagged U.S. real GDP should help predict the real price of oil. Recent research by
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- Kilian and Murphy (2010)
- Suffix
- has shown that the real price of oil indeed contains an asset price component, but that this component most of the time explains only a small fraction of the historical variation in the real price of oil.

- In-text reference with the coordinate start=46985
- Prefix
- This possibility is more than a theoretical curiosity in our context. Recent models of the determination of the real price of oil after 1973 have stressed that this price is determined in global markets (see, e.g.,
- Exact
- Kilian 2009a; Kilian and Murphy 2010).
- Suffix
- In particular, the demand for oil depends not merely on U.S. demand, but on global demand. The bivariate model for the real price of oil and U.S. real GDP by construction omits fluctuations in real GDP in the rest of the world.

- In-text reference with the coordinate start=108202
- Prefix
- These models produce empirically plausible estimates of the impact of demand and supply shocks in the oil market. A natural conjecture is that such models may also have value for forecasting. Here we focus on the reduced-form representation of the VAR model in
- Exact
- Kilian and Murphy (2010).
- Suffix
- The sample period is 1973.2-2009.8. The variables in this model include the percent change in global crude oil production, the global real activity measure we already discussed in section 4, the log of the real price of oil, and a proxy for the change in global above-ground crude oil inventories.

- In-text reference with the coordinate start=108565
- Prefix
- The variables in this model include the percent change in global crude oil production, the global real activity measure we already discussed in section 4, the log of the real price of oil, and a proxy for the change in global above-ground crude oil inventories. For further discussion of the data see
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- Kilian and Murphy (2010).
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- The VAR model may be consistently estimated without taking a stand on whether the real price of oil is I(0) or I(1) (see Sims, Stock and Watson 1990). We focus on recursive rather than rolling regression forecasts throughout this section.

- In-text reference with the coordinate start=109565
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- For that reason unrestricted VAR models are rarely used in applied forecasting. They nevertheless provide a useful point of departure. The upper panel of Table 13 shows results for unrestricted VAR models with 12 lags. Column (1) corresponds to the four-variable model used in
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- Kilian and Murphy (2010).
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- Table 13 shows that this unrestricted VAR forecast has lower recursive MSPE than the no-change forecast at all horizons but one and nontrivial directional accuracy. 29 Despite the lack of parsimony, the reductions in the MSPE are somewhat larger than for the AR and ARMA models in Table 12.

- In-text reference with the coordinate start=119283
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- At longer horizons the MSPE reductions diminish even for the best VAR models. Beyond one year, the no-change forecast usually has lower MSPE than the VAR model. Baumeister and Kilian also show that VAR forecasting models based on
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- Kilian and Murphy (2010)
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- exhibit significantly improved directional accuracy. The improved directional accuracy persists even at horizons at which the MSPE gains have vanished. The success ratios range from 0.51 to 0.60, depending on the model specification and horizon. 8.2.

- In-text reference with the coordinate start=124661
- Prefix
- Structural VAR Forecasts of the Real Price of Oil Recent research has shown that historical fluctuations in the real price of oil can be decomposed into the effects of distinct oil demand and oil supply shocks associated with unpredictable shifts in global oil production, real activity and a forward-looking or speculative element in the real price of oil (see, e.g.,
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- Kilian and Murphy 2010).
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- Changes in the composition of these shocks help explain why conventional regressions of macroeconomic aggregates on the price of oil tend to be unstable. They also are potentially important in interpreting oil price forecasts.

- In-text reference with the coordinate start=125045
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- They also are potentially important in interpreting oil price forecasts. In section 8 we showed that recursive forecasts of the real price of oil based on the type of oil market VAR model proposed in
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- Kilian and Murphy (2010)
- Suffix
- for the purpose of structural analysis are not necessarily inferior to simple no-change forecasts. The case for the use of VAR models, however, does not rest on their predictive accuracy alone. Policymakers expect oil price forecasts to be interpretable in light of an economic model.

- In-text reference with the coordinate start=126022
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- It requires a fully structural VAR model (see Waggoner and Zha 1999). In this section we illustrate how to generate such projections from the structural moving average representation of the VAR model of
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- Kilian and Murphy (2010)
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- estimated on data extending to 2009.8. The discussion closely follows Baumeister and Kilian (2011). This model allows the identification of three structural shocks: (1) a shock to the flow of the production of crude oil (“flow supply shock), (2) a shock to the flow demand for crude oil and other industrial commodities (“flow demand shock”) that reflects unexpected fluctuations in the global bu

- In-text reference with the coordinate start=128133
- Prefix
- The first scenario involves a successful stimulus to U.S. oil production, as had been considered by the Obama administration prior to the 2010 oil spill in the Gulf of Mexico. Here we consider the likely effects of a 20% increase in U.S. crude oil output in 2009.9, after the estimation sample of
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- Kilian and Murphy (2010)
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- ends. This is not to say that such a dramatic and sudden increase would be feasible, but that it would be a best-case scenario. Such a U.S. oil supply stimulus would translate to a 1.5% increase in world oil production, which is well within the variation of historical data.

- In-text reference with the coordinate start=130066
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- Alternatively, one could express these results relative to the unconditional VAR forecast. Finally, we consider the possibility of a speculative frenzy such as occurred starting in mid-1979 after the Iranian Revolution (see
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- Kilian and Murphy 2010).
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- This scenario involves feeding into the model future structural shocks corresponding to the sequence of speculative demand shocks that occurred between 1979.1 and 1980.2 and were a major contributor to the 1979/80 oil price shock episode.

- In-text reference with the coordinate start=44356
- 79
- Kilian, L., Rebucci, A., and N. Spatafora (2009), “Oil Shocks and External Balances,” Journal of International Economics, 77, 181-194.

Total in-text references: 1- In-text reference with the coordinate start=40258
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- In other words, autoregressive or moving average time series processes are inappropriate for these data and tests based on such models have to be viewed with 11 Although the U.K. has been exporting crude oil starting in the late 1970s, its share of petroleum exports is too low to consider the U.K. a commodity exporter (see
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- Kilian, Rebucci and Spatafora 2009).
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- caution. This problem with the pre-1973 data may be ameliorated by deflating the nominal price of oil, which renders the oil price data continuous and more amenable to VAR analysis (see Figure 2). Additional problems arise, however, when combining oil price data generated by a discrete-continuous choice process with data from the post-Texas Railroad Commission era that are fully continuous.

- In-text reference with the coordinate start=40258
- 80
- Kilian, L., and C. Vega (2010), “Do Energy Prices Respond to U.S. Macroeconomic News? A Test of the Hypothesis of Predetermined Energy Prices,” Review of Economics and Statistics, 93, 660-671.

Total in-text references: 2- In-text reference with the coordinate start=29784
- Prefix
- This line of reasoning is familiar from the analysis of stock and bond prices as well as exchange rates. 7 In the latter case, the endogeneity of the nominal price of oil with respect to the U.S. economy implies that lagged changes in U.S. macroeconomic aggregates have predictive power for the nominal price of oil in the post-1973 data (see, e.g., Cooley and LeRoy 1985). A recent study by
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- Kilian and Vega (2010)
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- helps resolve the question of which interpretation is more appropriate. Kilian and Vega find no evidence of systematic feedback from news about a wide range of U.S. macroeconomic aggregates to the nominal price of oil within a month.

- In-text reference with the coordinate start=82023
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- Note that the daily data are sparse in that there are many days for which no price quotes exist. We eliminate these dates from the sample and stack the remaining observations similar to the approach taken in
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- Kilian and Vega (2010)
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- in the context of modeling the impact of U.S. macroeconomic news on the nominal price of oil. Table 9 summarizes our findings. The MSPE ratios in Table 9 indicate somewhat larger gains in forecasting accuracy from using oil futures prices than in Tables 4 through 8.

- In-text reference with the coordinate start=29784
- 81
- Kilian, L., and R. Vigfusson (2010a), “Are the Responses of the U.S. Economy Asymmetric in Energy Price Increases and Decreases?” mimeo, Department of Economics, University of Michigan.

Total in-text references: 3- In-text reference with the coordinate start=12273
- Prefix
- The net oil price increase is a censored predictor that assigns zero weight to net oil price decreases. There is little evidence that this type of asymmetry is reflected in the responses of U.S. real GDP to innovations in the real price of oil, as documented in
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- Kilian and Vigfusson (2010a,b),
- Suffix
- but Hamilton (2010) suggests that the net oil price increase specification is best thought of as a parsimonious forecasting device. We provide a comprehensive analysis of this conjecture. Point forecasts of the price of oil are important, but they fail to convey the large uncertainty associated with oil price forecasts.

- In-text reference with the coordinate start=25916
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- Although Hamilton (2003) applied this transformation to the nominal price of oil, several other studies have recently explored models that apply the same transformation to the real price of oil (see, e.g.,
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- Kilian and Vigfusson 2010a; Herrera, Lagalo and Wada 2010).
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- prior to 2003. This finding is also consistent with the empirical results in Baumeister and Peersman (2010). For now we set aside all nonlinear transformations of the price of oil and focus on linear forecasting models for the nominal price of oil and for the real price of oil.

- In-text reference with the coordinate start=143491
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- Both the nonlinearity postulated in Edelstein and Kilian (2009) and that postulated in Ramey and Vine (2010) is incompatible with the specific nonlinearity embodied in the models of Mork (1989) and Hamilton (1996, 2003). In fact, the aforementioned papers rely on linear regressions after adjusting the energy price data. augmented by other variables. Recently,
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- Kilian and Vigfusson (2010a)
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- have shown that impulse response estimates from VAR models involving censored oil price variables are inconsistent even when equation (18) is correctly specified. Specifically, that paper demonstrated, first, that asymmetric models of the transmission of oil price shocks cannot be represented as censored oil price VAR models and are fundamentally misspecified whether the data generating proces

- In-text reference with the coordinate start=12273
- 82
- Kilian, L., and R. Vigfusson (2010b), “Nonlinearities in the Oil Price-Output Relationship,” forthcoming: Macroeconomic Dynamics.

Total in-text references: 5- In-text reference with the coordinate start=12273
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- The net oil price increase is a censored predictor that assigns zero weight to net oil price decreases. There is little evidence that this type of asymmetry is reflected in the responses of U.S. real GDP to innovations in the real price of oil, as documented in
- Exact
- Kilian and Vigfusson (2010a,b),
- Suffix
- but Hamilton (2010) suggests that the net oil price increase specification is best thought of as a parsimonious forecasting device. We provide a comprehensive analysis of this conjecture. Point forecasts of the price of oil are important, but they fail to convey the large uncertainty associated with oil price forecasts.

- In-text reference with the coordinate start=20194
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- Clearly, one would not want to interpret such an episode as an oil price shock involving a doubling of the 4 For further discussion of the trade-offs between alternative oil price definitions from an economic point of view see
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- Kilian and Vigfusson (2010b).
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- 5 For a review of the relationship between the concepts of (strict) exogeneity and predictability in linear models see Cooley and LeRoy (1985). nominal price of oil. Indeed, economic models of the impact of the price of oil on the U.

- In-text reference with the coordinate start=41757
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- This instability manifests itself in a structural break in the predictive regressions commonly used to test for lagged potentially nonlinear feedback from the real of price of oil to real GDP growth (see, e.g., Balke, Brown and Yücel 2002). The p-value for the null hypothesis that there is no break in 1973.Q4 in the coefficients of this predictive regression is 0.001 (see
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- Kilian and Vigfusson 2010b).
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- 12 For that reason, regression estimates of the relationship between the real price of oil and domestic macroeconomic aggregates obtained from the entire post-war period are not informative about the strength of these relationships in post-1973 data. 13 In the analysis of the real price of oil below we therefore restrict the evaluation period to start no earlier than 1973.1. 4.2.

- In-text reference with the coordinate start=148426
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- Below we therefore consider specifications with and without imposing exogeneity. In Table 19, we investigate whether there are MSPE reductions associated with the use of censored oil price variables at horizons 1,..., 8 ,h drawing on the analysis in
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- Kilian and Vigfusson (2010b,
- Suffix
- c). For completeness, we also include results for the percent increase specification proposed in Mork (1989), the forecasting performance of which has not been investigated to date. We consider nonlinear models based on the real price of oil as in Kilian and Vigfusson and nonlinear models based on the nominal price of oil as in Hamilton (2003).

- In-text reference with the coordinate start=170261
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- Some progress in this direction may be expected from ongoing work conducted by Anderson, Kellogg and Sallee (2010) based on the distribution of Michigan consumer expectations of 5-year-ahead gasoline prices. For further discussion of this point also see
- Exact
- Kilian and Vigfusson (2010b).
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- 12.3. Quantifying Oil Price Risks Although oil price volatility shifts play an important role in discussions of the impact of oil price shocks, it is important to keep in mind that volatility measures are not in general useful measures of the price risks faced by either producers or consumers of crude oil (or of refined products).

- In-text reference with the coordinate start=12273
- 83
- Kilian, L., and R. Vigfusson (2010c), “Do Net Oil Price Increases Help Forecast U.S. Real GDP?” mimeo, Department of Economics, University of Michigan.

Total in-text references: 1- In-text reference with the coordinate start=135875
- Prefix
- The baseline results are for the U.S. refiners’ acquisition cost for imported crude oil. Toward the end of the section we discuss how these results are affected by other oil price choices. Our discussion draws on results in
- Exact
- Kilian and Vigfusson (2010c).
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- 11.1. Linear Autoregressive Models A natural starting point is a linear VAR(p) model for the real price of oil and for U.S. real GDP expressed in quarterly percent changes. The general structure of the model is 1()tttxBLxe, where [,] ,tttxry tr denotes the log of real price of oil, ty the log of real GDP, is the difference operator, tethe regression error, and 21 ( )12 3.

- In-text reference with the coordinate start=135875
- 84
- Knetsch, T.A. (2007), “Forecasting the Price of Oil via Convenience Yield Predictions,” Journal of Forecasting, 26, 527-549.

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- Prefix
- Such attitudes have persisted notwithstanding recent empirical evidence to the contrary and notwithstanding the development of theoretical models aimed at explaining the lack of predictive ability of oil futures prices and spreads (see, e.g.,
- Exact
- Knetsch 2007; Alquist and Kilian 2010).
- Suffix
- Interestingly, the conventional wisdom in macroeconomics and finance is at odds with long-held views about storable commodities in agricultural economics. For example, Peck (1985) emphasized that “expectations are reflected nearly equally in current and in futures prices.

- In-text reference with the coordinate start=57303
- 85
- Koop, G., Pesaran M.H., and S.M. Potter (1996), “Impulse Response Analysis in Nonlinear Multivariate Models,” Journal of Econometrics, 74, 119‐147.

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- Prefix
- Second, standard approaches to the construction of structural impulse responses in this literature are invalid, even when applied to correctly specified models. Instead, Kilian and Vigfusson proposed a modification of the procedure discussed in
- Exact
- Koop, Pesaran and Potter (1996).
- Suffix
- Third, standard tests for asymmetry based on the slope coefficients of singleequation predictive models are neither necessary nor sufficient for judging the degree of asymmetry in the structural response functions, which is the question of ultimate interest to users of these models.

- In-text reference with the coordinate start=144281
- 86
- Leamer, E.E. (1978), Specification Searches: Ad hoc Inference with Nonexperimental Data, New York: Wiley-Interscience.

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- Prefix
- As is well known, for sufficiently large sample sizes, any null hypothesis is bound to be rejected at conventional significance levels, making it inappropriate to apply the same significance level as in Tables 4 through 8. In recognition of this problem,
- Exact
- Leamer (1978,
- Suffix
- p. 108-120) proposes a rule for constructing samplesize dependent critical values. For example, for the F-statistic, the appropriate level of statistical significance is (1/ )1(1)(1),1,.tfcdfttt For 216,t as in Table 4, this rule of thumb implies a threshold for rejecting the null hypothesis of0.0209.

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- 88
- Machina, M.J., and M. Rothschild (1987), “Risk,” in Eatwell, J., Millgate, M., and P. Newman (eds.), The New Palgrave Dictionary of Economics, London: MacMillan, 203-205.

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- Prefix
- One requirement is that the measure of risk must be related to the probability distribution ()Fof the random variable of interest; the other requirement is that it must be linked to the preferences of the user, typically parameterized by a loss function (see
- Exact
- Machina and Rothschild 1987).
- Suffix
- Except in special cases these requirements rule out commonly used measures of risk based on the predictive distribution alone such as the sample moments, sample quantiles or the value at risk. In deriving appropriate risk measures that characterize the predictive distribution for the real price of oil, it is useful to start with the loss function.

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- 89
- Marcellino, M., Stock, J.H., and M.W. Watson (2006), “A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series,” Journal of Econometrics, 135, 499-526.

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- Prefix
- no-change forecast may lower the asymptotic MSPE even relative to the correctly specified non-random walk model, provided the local drift parameter governing the predictive relationship is close enough to zero (see, e.g., Inoue and Kilian (2004b), Clark and McCracken 2010). 26 The refiners’ acquisition cost was extrapolated back to 1973.2 as in Barsky and Kilian (2002). selection (see
- Exact
- Inoue and Kilian 2006; Marcellino, Stock and Watson 2006).
- Suffix
- We search over p0,...,12 . The forecast accuracy results are robust to allowing for a larger upper bound. There are no theoretical results in the forecasting literature on how to assess the null of equal predictive accuracy when comparing iterated AR or ARMA forecasts to the no-change forecast.

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- 90
- Mork, K.A. (1989), “Oil and the Macroeconomy. When Prices Go Up and Down: An Extension of Hamilton’s Results,” Journal of Political Economy, 97, 740‐744.

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- Prefix
- That adjustment as well serves to remove a nonlinearity in the transmission process. Both the nonlinearity postulated in Edelstein and Kilian (2009) and that postulated in Ramey and Vine (2010) is incompatible with the specific nonlinearity embodied in the models of
- Exact
- Mork (1989) and Hamilton (1996, 2003).
- Suffix
- In fact, the aforementioned papers rely on linear regressions after adjusting the energy price data. augmented by other variables. Recently, Kilian and Vigfusson (2010a) have shown that impulse response estimates from VAR models involving censored oil price variables are inconsistent even when equation (18) is correctly specified.

- In-text reference with the coordinate start=148556
- Prefix
- In Table 19, we investigate whether there are MSPE reductions associated with the use of censored oil price variables at horizons 1,..., 8 ,h drawing on the analysis in Kilian and Vigfusson (2010b, c). For completeness, we also include results for the percent increase specification proposed in
- Exact
- Mork (1989),
- Suffix
- the forecasting performance of which has not been investigated to date. We consider nonlinear models based on the real price of oil as in Kilian and Vigfusson and nonlinear models based on the nominal price of oil as in Hamilton (2003).

- In-text reference with the coordinate start=149141
- Prefix
- The unrestricted multivariate nonlinear forecasting model takes the form 44 111,12,1, 11 44 4 221,22,2, 11 1 titiitit ii titiitiitit ii i rBrBye yBrByr e (20) where ,,3,,1,,,netyrnetyrttttrrrr(0)tttrrIr as in
- Exact
- Mork (1989), and
- Suffix
- I(•) denotes the indicator function. Analogous nonlinear forecasting models may be constructed based on the nominal price of oil, denoted in logs as :ts 44 111,12,1, 11 44 4 221,22,2, 11 1 titiitit ii titiitii tit ii i sBsBye yBsBys e (20) where ,,3,,1 ,,. netyrnetyr ssttttss In addition, we consider

- In-text reference with the coordinate start=152570
- Prefix
- accounting for 11 percentage points by itself) and the omission of lagged percent changes in the nominal price of oil (accounting for 4 percentage points by itself) are mainly responsible for the additional gain in accuracy; the imposition of exogeneity plays no role. Accuracy gains at slightly shorter or longer horizons are closer to 10%. Second, neither the percent increase model based on
- Exact
- Mork (1989)
- Suffix
- nor the one-year net increase model motivated by Hamilton (1996) is more accurate than the AR(4) benchmark at the one-quarter horizon. This is true regardless of whether the price of oil is specified in nominal or real terms and regardless of what additional restrictions we impose.

- In-text reference with the coordinate start=143307
- 91
- Peck, A.E. (1985), “Economic Role of Traditional Commodity Futures Markets,” in A.E. Peck (ed.): Futures Markets: Their Economic Role, Washington, DC: American Enterprise Institute for Public Policy Research, 1-81.

Total in-text references: 1- In-text reference with the coordinate start=57511
- Prefix
- evidence to the contrary and notwithstanding the development of theoretical models aimed at explaining the lack of predictive ability of oil futures prices and spreads (see, e.g., Knetsch 2007; Alquist and Kilian 2010). Interestingly, the conventional wisdom in macroeconomics and finance is at odds with long-held views about storable commodities in agricultural economics. For example,
- Exact
- Peck (1985)
- Suffix
- emphasized that “expectations are reflected nearly equally in current and in futures prices. In this sense cash prices will be nearly as good predictions of subsequent cash prices as futures prices”, echoing in turn the discussion in Working (1942) who was critical of the “general opinion among economists that prices of commodity futures are ... the market expression of consciously formed opin

- In-text reference with the coordinate start=57511
- 92
- Pesaran, M.H., and A. Timmermann (2009), “Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, 104, 325 337.

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- Prefix
- The assessment of which forecasting model is most accurate may depend on the loss function of the forecaster (see Elliott and Timmermann 2008). We report results for the MSPE and the relative frequency with which a forecasting model correctly predicts the sign of the change in the spot price based on the success ratio statistic of
- Exact
- Pesaran and Timmermann (2009).
- Suffix
- We formally test the null hypothesis that a given candidate forecasting model is as accurate as the random walk without drift against the alternative that the candidate model is more accurate than the no-change forecast.

- In-text reference with the coordinate start=63988
- 93
- Pindyck, R.S. (1991), “Irreversibility, Uncertainty and Investment,” Journal of Economic Literature, 29, 1110‐1148.

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- Prefix
- Real Oil Price Volatility Interest in the volatility of oil prices also has been prompted by research aimed at establishing a direct link from oil price volatility to business cycle fluctuations in the real economy. For example,
- Exact
- Bernanke (1983) and Pindyck (1991)
- Suffix
- showed that the uncertainty of the price of oil (measured by the volatility of the price of oil) matters for investment decisions if firms contemplate an irreversible investment, the cash flow of which depends on the price of oil.

- In-text reference with the coordinate start=168014
- 94
- Ramey, V.A., and D.J. Vine (2010), “Oil, Automobiles, and the U.S. Economy: How Much Have Things Really Changed,” forthcoming: NBER Macroeconomics Annual.

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- Prefix
- Hamilton’s line of reasoning has prompted many researchers to construct asymmetric responses to positive and negative oil price innovations from censored oil price VAR models. Censored oil price VAR models refer to linear VAR models for ,,3 [,], netyr possibly sytt 31 In related work,
- Exact
- Ramey and Vine (2010)
- Suffix
- propose an alternative adjustment to the price of gasoline that reflects the time cost of queuing in gasoline markets during the 1970s. That adjustment as well serves to remove a nonlinearity in the transmission process.

- In-text reference with the coordinate start=143213
- Prefix
- 31 In related work, Ramey and Vine (2010) propose an alternative adjustment to the price of gasoline that reflects the time cost of queuing in gasoline markets during the 1970s. That adjustment as well serves to remove a nonlinearity in the transmission process. Both the nonlinearity postulated in Edelstein and Kilian (2009) and that postulated in
- Exact
- Ramey and Vine (2010)
- Suffix
- is incompatible with the specific nonlinearity embodied in the models of Mork (1989) and Hamilton (1996, 2003). In fact, the aforementioned papers rely on linear regressions after adjusting the energy price data. augmented by other variables.

- In-text reference with the coordinate start=142882
- 95
- Ravazzolo, F., and P. Rothman (2010), “Oil and U.S. GDP: A Real Time Out-of-Sample Examination,” mimeo, Norges Bank.

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- Prefix
- additional question would be how the results of the forecast accuracy comparison for U.S. real GDP growth would have changed, had we only used data sets actually available as of the time the forecast is generated. This remains an open question at this point.32 32 Some preliminary evidence on this question has been provided by
- Exact
- Ravazzolo and Rothman (2010) and
- Suffix
- by Carlton (2010). It is not straightforward to compare their results to those in Tables 19 and 20, however. Not only is their analysis based on one-step-ahead real GDP growth forecasts from single-equation predictive models evaluated at the relevant forecasting horizon (rather than iterated forecasts from multivariate models), but it is based on a sample period that includes pre-1973 data.

- In-text reference with the coordinate start=159923
- 98
- Sims, C.A., Stock, J.H., and M.W. Watson (1990), “Inference in Linear Time Series Models with Some Unit Roots,” Econometrica, 58, 113-144.

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- Prefix
- in global crude oil production, the global real activity measure we already discussed in section 4, the log of the real price of oil, and a proxy for the change in global above-ground crude oil inventories. For further discussion of the data see Kilian and Murphy (2010). The VAR model may be consistently estimated without taking a stand on whether the real price of oil is I(0) or I(1) (see
- Exact
- Sims, Stock and Watson 1990).
- Suffix
- We focus on recursive rather than rolling regression forecasts throughout this section. This approach makes sense in the absence of structural change, given the greater efficiency of recursive regressions and the small sample size. 28 A natural starting point for the forecast accuracy comparison is the unrestricted VAR model.

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- 99
- Skeet, I. (1988), OPEC: Twenty-Five Years of Prices and Politics. Cambridge: Cambridge University Press.

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- First, there is little evidence to support the notion that OPEC has been successfully acting as a cartel in the 1970s and early 1980s, and the role of OPEC has diminished further since 1986 (see, e.g.,
- Exact
- Skeet 1988; Smith 2005; Almoguera, Douglas and Herrera 2010).
- Suffix
- Second, even if we were to accept the notion that an OPEC cartel sets the nominal price of oil, economic theory predicts that this cartel price will endogenously respond to U.S. macroeconomic conditions.

- In-text reference with the coordinate start=21073
- 100
- Smith, J.L. (2005), “Inscrutable OPEC? Behavioral Tests of the Cartel Hypothesis,” Energy Journal, 26, 51-82.

Total in-text references: 1- In-text reference with the coordinate start=21073
- Prefix
- First, there is little evidence to support the notion that OPEC has been successfully acting as a cartel in the 1970s and early 1980s, and the role of OPEC has diminished further since 1986 (see, e.g.,
- Exact
- Skeet 1988; Smith 2005; Almoguera, Douglas and Herrera 2010).
- Suffix
- Second, even if we were to accept the notion that an OPEC cartel sets the nominal price of oil, economic theory predicts that this cartel price will endogenously respond to U.S. macroeconomic conditions.

- In-text reference with the coordinate start=21073
- 101
- Stock, J.H., and M.W. Watson (1999), “Forecasting Inflation,” Journal of Monetary Economics, 44, 293-335.

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- Prefix
- This is a broad measure of monthly real economic activity in the United States obtained from applying principal component analysis to a wide range of monthly indicators of real activity expressed in growth rates (see
- Exact
- Stock and Watson 1999).
- Suffix
- As in the case of quarterly U.S. real GDP, there is no evidence of Granger causality. If we rely on U.S. industrial production as the predictor, there is weak evidence of feedback to the domestic price of oil for the LT model.

- In-text reference with the coordinate start=122788
- Prefix
- They provide empirical examples in which the forecasting accuracy of that model in recursive settings is not only superior to unrestricted VAR models, but is comparable to that of single-equation dynamic factor models (see
- Exact
- Stock and Watson 1999).
- Suffix
- Table 16 compares the forecasting accuracy of this approach with that of the unrestricted VAR models considered in Tables 13 and 15. In all cases, we shrink the model parameters toward a white noise prior mean with the desired degree of shrinkage being determined by the data-based procedure in Giannone et al. (2010).

- In-text reference with the coordinate start=51351
- 102
- Svensson, L.E.O. (2005), “Oil Prices and ECB Monetary Policy,” mimeo, Department of Economics, Princeton University. See: http://www.princeton.edu/svensson/

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- Prefix
- Forecasts of the spot price of oil are used as inputs in the macroeconomic forecasting exercises that these institutions produce. For example, the European Central Bank (ECB) employs oil futures prices in constructing the inflation and output-gap forecasts that guide monetary policy (see
- Exact
- Svensson 2005).
- Suffix
- Likewise the IMF relies on futures prices as a predictor of future spot prices (see, e.g., International Monetary Fund 2005, p. 67; 2007, p. 42). Futures-based forecasts of the price of oil also play a role in policy discussions at the Federal Reserve Board.

- In-text reference with the coordinate start=56452
- 103
- Tinbergen, J. (1959). “Tonnage and Freight” in: Jan Tinbergen Selected Papers, Amsterdam: North Holland, 93-111.

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- Prefix
- -Horizon Forecasts of the Nominal Price of Oil The most common approach to forecasting the nominal price of oil is to treat the price of the oil 16 This index is constructed from ocean shipping freight rates. The idea of using fluctuations in shipping freight rates as indicators of shifts in the global real activity dates back to
- Exact
- Isserlis (1938) and Tinbergen (1959).
- Suffix
- The panel of monthly freight-rate data underlying the global real activity index was collected manually from Drewry’s Shipping Monthly using various issues since 1970. The data set is restricted to dry cargo rates.

- In-text reference with the coordinate start=54505
- 104
- Waggoner, D.F., and T. Zha (1999), “Conditional Forecasts in Dynamic Multivariate Models,” Review of Economics and Statistics, 81, 639-651.

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- Prefix
- Questions of interest include, for example, what effects an unexpected slowing of Asian growth would have on the forecast of the real price of oil; or what the effect would be of an unexpected decline in global oil production associated with peak oil. Answering questions of this type is impossible using reduced-form time series models. It requires a fully structural VAR model (see
- Exact
- Waggoner and Zha 1999).
- Suffix
- In this section we illustrate how to generate such projections from the structural moving average representation of the VAR model of Kilian and Murphy (2010) estimated on data extending to 2009.8. The discussion closely follows Baumeister and Kilian (2011).

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- 105
- Working, H. (1942), “Quotations on Commodity Futures as Price Forecasts,” Econometrica, 16, 39-52.

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- Prefix
- For example, Peck (1985) emphasized that “expectations are reflected nearly equally in current and in futures prices. In this sense cash prices will be nearly as good predictions of subsequent cash prices as futures prices”, echoing in turn the discussion in
- Exact
- Working (1942)
- Suffix
- who was critical of the “general opinion among economists that prices of commodity futures are ... the market expression of consciously formed opinions on probable prices in the future” whereas “spot prices are not generally supposed to reflect anticipation of the future in the same degree as futures prices”.

- In-text reference with the coordinate start=57757
- 106
- Wu, T., and A. McCallum (2005), “Do Oil Futures Prices Help Predict Future Oil Prices?” Federal Reserve Bank of San Francisco Economic Letter, 2005-38.

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- Prefix
- While economists have used survey data extensively in measuring the risk premium embedded in foreign exchange futures, this approach has not been applied to oil futures, with the exception of recent work by
- Exact
- Wu and McCallum (2005).
- Suffix
- Yet another approach is to exploit the implication of the Hotelling (1931) model that the price of oil should grow at the rate of interest. Finally, we also consider forecasting models that adjust the no-change forecast for inflation expectations and for recent percent changes in other nominal prices. 5.2.1.

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- 107
- Zagaglia, P. (2010), “Macroeconomic Factors and Oil Futures Prices: A Data-Rich Model,” Energy Economics, 32, 409-417.

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- Prefix
- Both of these effects may undermine the predictive power of the price of oil for macroeconomic aggregates as well as the explanatory power of theoretical models based on oil price forecasts. Third, we have deliberately refrained from exploring the use of factor models for forecasting the price of oil. In related work,
- Exact
- Zagaglia (2010)
- Suffix
- reports some success in using a factor model in forecasting the nominal price of oil at short horizons, although his evaluation period is limited to early 2003 to early 2008, given the data limitations, and it is unclear how sensitive the results would be to extending the evaluation period.

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- Short of developing a comprehensive worldwide data set of real aggregates at monthly frequency, it is not clear whether there are enough predictors available for reliable real-time estimation of the factors. For example, drawing excessively on U.S. real aggregates as in
- Exact
- Zagaglia (2010)
- Suffix
- is unlikely to be useful for forecasting the global price of oil for the reasons discussed in section 4. Using a cross-section of data on energy prices, quantities, and other oil-market related indicators may be more promising, but almost half of the series used by Zagaglia are specific to the United States and unlikely to be representative of global markets. 14.

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