- 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
- 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
- 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
- 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).
- Suffix
- 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
- 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
- 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),
- Suffix
- 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. The latter question is the main focus of sections 5 through 8.

- 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
- 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.

Total in-text references: 6- In-text reference with the coordinate start=13716
- Prefix
- 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
- Prefix
- 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
- 50
- 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
- 51
- 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
- 53
- 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=141311
- 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 Edelstein and
- Exact
- 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
- 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
- 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.

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
- 60
- Hotelling, H. (1931), “The Economics of Exhaustible Resources,” Journal of Political Economy, 39, 137-175.

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- 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
- 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.,
- Exact
- Kahn (1986),
- Suffix
- Davis and Kilian (2010). 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;
- Suffix
- Davis and Kilian 2010; Allcott and Wozny 2010). 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.

- In-text reference with the coordinate start=6256
- 68
- 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=6389
- Prefix
- We document strong evidence of predictability 1 See, e.g., Kahn (1986), Davis and Kilian (2010). 2 See, e.g., Goldberg (1998), Allcott and Wozny (2010), Busse, Knittel and Zettelmeyer (2010),
- Exact
- 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=168582
- Prefix
- 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.
- Exact
- Kellogg (2010)
- Suffix
- 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
- Prefix
- 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,
- Exact
- Kellogg (2010)
- Suffix
- 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)
- Suffix
- 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=6389
- 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).
- Suffix
- 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.

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- 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
- Exact
- Kilian (2008b;
- Suffix
- 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.

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- 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;
- Suffix
- Kilian and Murphy 2010). 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),
- Suffix
- 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: 10- In-text reference with the coordinate start=6279
- 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
- Exact
- Kilian (2010).
- Suffix
- 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=57329
- 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., Knetsch 2007; Alquist and
- Exact
- 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=59222
- 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 Alquist and
- Exact
- 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=61276
- 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 Alquist and
- Exact
- 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=61493
- Prefix
- , 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 Alquist and
- Exact
- 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=66554
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- 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 Alquist and
- Exact
- 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=86247
- 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 Alquist and
- Exact
- 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=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
- Exact
- Kilian 2010).
- Suffix
- 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=93393
- 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., Kahn 1986; Davis and
- Exact
- Kilian 2010;
- Suffix
- Allcott and Wozny 2010). 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.

- 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
- Exact
- Kilian 2010).
- Suffix
- 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=6279
- 84
- Knetsch, T.A. (2007), “Forecasting the Price of Oil via Convenience Yield Predictions,” Journal of Forecasting, 26, 527-549.

Total in-text references: 1- 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;
- Suffix
- 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, Peck (1985) emphasized that “expectations are reflected nearly equally in current and in futures prices.

- In-text reference with the coordinate start=57303
- 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.

- In-text reference with the coordinate start=83840
- 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
- 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
- 99
- Skeet, I. (1988), OPEC: Twenty-Five Years of Prices and Politics. Cambridge: Cambridge University Press.

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- 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;
- Suffix
- Almoguera, Douglas and Herrera 2010). 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.

- 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.

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- 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;
- Suffix
- Almoguera, Douglas and Herrera 2010). 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.

- In-text reference with the coordinate start=21073
- 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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- -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
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- Isserlis (1938) and Tinbergen (1959).
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- 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.

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

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- 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
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- Working (1942)
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- 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”.

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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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- 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,
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- Zagaglia (2010)
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- 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
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- Zagaglia (2010)
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- 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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