The 41 references with contexts in paper Ron Alquist, Lutz Kilian, Robert J. Vigfusson (2011) “Forecasting the Price of Oil” / RePEc:bca:bocawp:11-15

4
Anatolyev, S. (2007), “Inference about Predictive Ability When There Are Many Predictors,” mimeo, New Economic School, Moscow.
Total in-text references: 1
  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

13
Bernanke, B.S. (1983), “Irreversibility, Uncertainty, and Cyclical Investment,” Quarterly Journal of Economics, 98, 85‐106.
Total in-text references: 1
  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.

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
  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.

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
  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.

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
  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.

19
Carlton, A.B. (2010), “Oil Prices and Real-Time Output Growth,” mimeo, Department of Economics, University of Houston.
Total in-text references: 1
  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.

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
  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.

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
  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).

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
  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.

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
  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.

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
  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.

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
  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.

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
  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

50
Hamilton, J.D. (1985), “Historical Causes of Postwar Oil Shocks and Recessions,” Energy Journal, 6, 97–116.
Total in-text references: 1
  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.

51
Hamilton, J.D. (1994), Time Series Analysis, Princeton, NJ: Princeton University Press.
Total in-text references: 1
  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.

52
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
  1. 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.

  2. 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.

  3. 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.

53
Hamilton, J. D. (2003) “What is an Oil Shock?” Journal of Econometrics, 113, 363–398.
Total in-text references: 10
  1. 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.

  2. 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.

  3. 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.

  4. 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,..., 8hand lag orders 1,..., 24 .pThe 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 ()() ().

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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.

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
  1. 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.

  2. 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

  3. 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).

55
Hamilton, J.D. (2010), “Nonlinearities and the Macroeconomic Effects of Oil Prices,” forthcoming: Macroeconomic Dynamics.
Total in-text references: 9
  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

57
Hendry, D. (2006), “Robustifying Forecasts from Equilibrium-Correction Systems,” Journal of Econometrics, 135, 399-426
Total in-text references: 1
  1. In-text reference with the coordinate start=68076
    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

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
  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).

60
Hotelling, H. (1931), “The Economics of Exhaustible Resources,” Journal of Political Economy, 39, 137-175.
Total in-text references: 2
  1. In-text reference with the coordinate start=7724
    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.

  2. 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.

64
Isserlis, L. (1938), “Tramp Shipping Cargoes and Freights,” Journal of the Royal Statistical Society, 101(1), 53-134.
Total in-text references: 1
  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.

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
  1. 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.

  2. 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.

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
  1. 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.

  2. 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

  3. 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.

  4. 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

70
Kilian, L. (2008a), “The Economic Effects of Energy Price Shocks,” Journal of Economic Literature, 46(4), 871-909.
Total in-text references: 1
  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.

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
  1. 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
    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.

  2. 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.

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
  1. 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

  2. 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.

  3. 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

  4. 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.

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
  1. 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

  2. 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.

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
  1. 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.

  2. 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.

  3. 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.

  4. 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: 

  5. 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.

  6. In-text reference with the coordinate start=66554
    Prefix
    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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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
  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.

86
Leamer, E.E. (1978), Specification Searches: Ad hoc Inference with Nonexperimental Data, New York: Wiley-Interscience.
Total in-text references: 1
  1. In-text reference with the coordinate start=83840
    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.

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.
Total in-text references: 4
  1. 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.

  2. 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).

  3. 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

  4. 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.

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
  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

93
Pindyck, R.S. (1991), “Irreversibility, Uncertainty and Investment,” Journal of Economic Literature, 29, 1110‐1148.
Total in-text references: 1
  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.

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.,
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    Skeet 1988; Smith 2005;
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    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.

100
Smith, J.L. (2005), “Inscrutable OPEC? Behavioral Tests of the Cartel Hypothesis,” Energy Journal, 26, 51-82.
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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;
    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.

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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    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
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    Svensson 2005).
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    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.

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.

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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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.