The 20 references with contexts in paper Christiane Baumeister, Lutz Kilian (2011) “Real-Time Forecasts of the Real Price of Oil” / RePEc:bca:bocawp:11-16

1
Alquist, R., and L. Kilian (2010), “What Do We Learn from the Price of Crude Oil Futures?” Journal of Applied Econometrics, 25, 539-573.
Total in-text references: 2
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    The Real-Time Data Set Unlike Carlton (2010) and Ravazzolo and Rothman (2010) our focus is not on real-time forecasting of U.S. real GDP growth on the basis of lagged oil prices, but rather on generating real-time forecasts of the real price of oil. Our analysis is more closely related to the recent literature on real-time forecasts of the nominal price of oil (see, e.g.,
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    Alquist and Kilian 2010;
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    Alquist, Kilian, and Vigfusson 2011). Although the real price of oil is one of the key variables in the model-based macroeconomic projections generated by central banks, private sector forecasters, and international organizations, there have been no studies to date of how best to forecast the real price of oil in real time.

  2. In-text reference with the coordinate start=50674
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    that the spot and 6 More specifically, the identifying assumptions are that: (1) a negative oil supply shock shifts the supply curve to the left along the oil demand curve, resulting in a decrease in oil production and an increase in the price of oil, which futures markets for crude oil are linked by an arbitrage condition (see
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    Alquist and Kilian 2010).
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    Thus, any speculation taking place in the oil futures market implies a shift in inventory demand in the spot market by construction. This fact allows us to abstract from the oil futures market altogether.

3
Baumeister, C., and G. Peersman (2010), “Sources of the Volatility Puzzle in the Crude Oil Market,” mimeo, Department of Economics, Ghent University.
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    in exploring the possible consequences of civil unrest in Libya, or in exploring how much a period of unexpectedly low global demand for crude oil caused by a global recession would lower the real price of oil. The construction of such forecast scenarios requires the use of structural econometric models. Structural models of the global market for crude oil have recently been developed by
    Exact
    Kilian (2009), Kilian and Murphy (2010, 2011), and Baumeister and Peersman (2010),
    Suffix
    among others. In this section, we focus on the structural vector autoregressive model proposed in Kilian and Murphy (2010). This model was designed to help us distinguish, in particular, between unexpected oil production shortfalls, unexpected changes in the global demand for crude oil driven by the global business cycle, and shocks to the demand for above-ground crude oil inventories driven

4
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=11853
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    These tools are designed to allow end-users to interpret oil price forecasts in light of economic models and to evaluate their sensitivity to alternative assumptions. The concluding remarks are in section 6. 2. The Real-Time Data Set Unlike
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    Carlton (2010) and Ravazzolo and Rothman (2010)
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    our focus is not on real-time forecasting of U.S. real GDP growth on the basis of lagged oil prices, but rather on generating real-time forecasts of the real price of oil. Our analysis is more closely related to the recent literature on real-time forecasts of the nominal price of oil (see, e.g.

5
Clark, T.E., and M. McCracken (2009), “Tests of Equal Predictive Ability with Real-Time Data,” Journal of Business and Economic Statistics, 27, 441-454.
Total in-text references: 2
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    The problem is that the underlying time series are not stationary because they include data that have been revised to different degrees (see, e.g., Koenig, Dolmas, and Piger 2003; Clements and Galvão 2010; Croushore 2011). This feature of the regression analysis violates the premise of standard asymptotic tests of equal predictive accuracy.
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    Clark and McCracken (2009)
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    recently proposed an alternative test of equal predictive accuracy for real-time data, the construction of which requires further assumptions on the nature of the data revisions and evidence that these assumptions are met in the real-time data.

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    proposed an alternative test of equal predictive accuracy for real-time data, the construction of which requires further assumptions on the nature of the data revisions and evidence that these assumptions are met in the real-time data. That test is not designed for iterated forecasts, however, and could not be applied in our context even if our real-time data satisfied the assumptions of
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    Clark and McCracken (2009).
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    Nor is it possible to rely on standard bootstrap methods to simulate the critical values of tests of equal predictive accuracy in our iterated real-time setting. In section 3.2, we will show, however, that the same regression models generate highly statistically significant rejections of the null of equal predictive accuracy when applied to ex-post revised data.

7
Croushore, D. (2006), “Forecasting with Real-Time Macroeconomic Data,” in: G. Elliott, C.W.J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, Amsterdam: North-Holland, 961-982.
Total in-text references: 1
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    Classification JEL : Q43, C53, E32 Classification de la Banque : Méthodes économétriques et statistiques; Questions internationales iv 1. Introduction The real-time nature of data used in forecasting has received increasing attention in recent years (see, e.g.,
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    Clements and Galvão 2010; Croushore 2006, 2011).
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    Although the real price of oil is one of the key variables in the model-based macroeconomic projections generated by central banks, private sector forecasters, and international organizations, there have been no studies to date of how best to forecast the real price of oil in real time.

8
Croushore, D. (2011), “Frontiers of Real-Time Data Analysis,” Journal of Economic Literature, 49, 72-100.
Total in-text references: 3
  1. In-text reference with the coordinate start=4869
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    Classification JEL : Q43, C53, E32 Classification de la Banque : Méthodes économétriques et statistiques; Questions internationales iv 1. Introduction The real-time nature of data used in forecasting has received increasing attention in recent years (see, e.g.,
    Exact
    Clements and Galvão 2010; Croushore 2006, 2011).
    Suffix
    Although the real price of oil is one of the key variables in the model-based macroeconomic projections generated by central banks, private sector forecasters, and international organizations, there have been no studies to date of how best to forecast the real price of oil in real time.

  2. In-text reference with the coordinate start=33562
    Prefix
    The reason is that such tests are not available. The problem is that the underlying time series are not stationary because they include data that have been revised to different degrees (see, e.g., Koenig, Dolmas, and Piger 2003;
    Exact
    Clements and Galvão 2010; Croushore 2011).
    Suffix
    This feature of the regression analysis violates the premise of standard asymptotic tests of equal predictive accuracy. Clark and McCracken (2009) recently proposed an alternative test of equal predictive accuracy for real-time data, the construction of which requires further assumptions on the nature of the data revisions and evidence that these assumptions are met in the real-time data.

  3. In-text reference with the coordinate start=73723
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    Our focus in this paper is on illustrating the use of structural models in constructing forecast scenarios rather than on advocating one type of structural model over another. 6. Conclusion The importance of real-time forecasting is well recognized in the literature (see, e.g.,
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    Croushore 2011).
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    Much of the work on real-time forecasting to date has focused on domestic macroeconomic aggregates. In contrast, our focus in this paper has been on generating real-time forecasts for the real price of oil, which is widely considered one of the key global macroeconomic indicators.

9
De Jong, P. (1987),”Rational Economic Data Revisions,” Journal of Business and Economic Statistics, 5, 539-548.
Total in-text references: 1
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    This data set allows the construction of real-time forecasts of the real price of oil from a variety of models. Both backcasting and nowcasting techniques are used to fill gaps in the real-time data. In section 2, we show that revisions of most oil market data represent “news” as defined in De
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    Jong (1987) and
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    Faust, Rogers, and Wright (2005). In other words, there is little scope for improving forecasts by modeling the revision process. This fact facilitates the construction of nowcasts to fill in gaps in the availability of real-time data for most series.

10
Diebold, F.X., and R.S. Mariano (1995), “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253-263.
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    The reductions in MSPE range from 0% to 9% with a tendency to increase with the forecasting horizon. Very similar results would be obtained using ex-post revised data. However, none of the reductions in MSPEs are statistically significant based on the
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    Diebold and Mariano (1995)
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    test. When forecasting the real WTI price instead, this futures-based forecasting method is even less successful. Only at horizons of 9 and 12 are there any noticeable reductions in the MSPE, and, again, none of the reductions are statistically significant.

12
Faust, J., Rogers, J., and J.H. Wright (2005), “News and Noise in G-7 GDP Announcements,” Journal of Money, Credit, and Banking, 37, 403-419.
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    This hypothesis implies that revisions must be mean zero because otherwise revisions would be predictable. It can be shown that there is no statistically significant bias in the revisions for any of the three transformed real-time variables. As noted by
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    Faust et al. (2005),
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    bias is the simplest form of predictability in revision. Even in the absence of bias, however, revisions may be predictable based on preliminary data releases. A more general specification of the news hypothesis implies that 0αβ== in ptttRXuαβ=++ where tu may be serially correlated.

14
Giannone, D., and L. Reichlin (2006), “Does Information Help Recover Structural Shocks from Past Observations,” Journal of the European Economic Association, 4, 455-465.
Total in-text references: 1
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    This fact allows us to abstract from the oil futures market altogether. Indeed, it can be shown that the structural shocks identified by this model are fundamental in that the oil futures spread does not Granger-cause the other variables included in the model (see
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    Giannone and Reichlin 2006).
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    The reduced-form representation of the Kilian and Murphy (2010) model corresponds to the four-variable VAR model we already considered in sections 2 and 3 and showed to be quite accurate compared with the no-change benchmark as well as competing models.

15
Kilian, L. (2008), “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: 1
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    such fears could be arbitrarily weak or strong, making it difficult to assess the quantitative importance of this channel, but the historical experience of earlier episodes in Figure 2 provides some guidance. One contagion scenario can be motivated by focusing on the surge in speculative demand that occurred preceding and following the invasion of Kuwait in August of 1990. As discussed in
    Exact
    Kilian (2008) and Kilian and Murphy (2010),
    Suffix
    among others, the invasion not only caused oil production in Kuwait and Iraq to cease, but raised concerns that Saudi Arabia and its smaller neighbors would be invaded next, causing a surge in speculative demand that only subsided after the U.

16
Kilian, L. (2009), “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=14653
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    Prior to 1996.1 this publication is not available in electronic form. The construction of the real-time data set from the historical issues of the Monthly Energy Review is described in detail below. The nominal shipping rate data are obtained from
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    Kilian (2009)
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    for 1973.1 through 1984.12 and are extrapolated through 2010.12 using the Baltic Dry Cargo Index (BDI) from Bloomberg. Real-time data for the monthly U.S. consumer price index are obtained from the Economic Indicators published by the Council of Economic Advisers.

  2. In-text reference with the coordinate start=22932
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    Finally, as is standard in the literature, the measure of global real activity is constructed by cumulating the growth rate of the index of nominal shipping rates, the resulting nominal index is deflated by the U.S. consumer price index, and a linear deterministic trend representing increasing returns to scale in ocean shipping is removed from the real index (see, e.g.,
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    Kilian 2009).
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    The resulting index is designed to capture business cycle fluctuations in global industrial commodity markets. In constructing the real-time version of this index of global real activity, the linear deterministic trend is recursively re-estimated in real time.

  3. In-text reference with the coordinate start=27866
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    Regression-Based Forecasts Table 1 summarizes the real-time forecast accuracy of ARMA and AR models of the real U.S. refiners’ acquisition cost for crude oil imports and of the four-variable VAR oil market model of Kilian and Murphy (2010). The VAR model includes the percent change in global crude oil production, the
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    Kilian (2009)
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    measure of global real activity (in deviations from trend), the change in global crude oil inventories and the real U.S. refiners’ acquisition cost for crude oil imports, as a measure of the real price of crude oil in global oil markets.

  4. In-text reference with the coordinate start=49032
    Prefix
    in exploring the possible consequences of civil unrest in Libya, or in exploring how much a period of unexpectedly low global demand for crude oil caused by a global recession would lower the real price of oil. The construction of such forecast scenarios requires the use of structural econometric models. Structural models of the global market for crude oil have recently been developed by
    Exact
    Kilian (2009), Kilian and Murphy (2010, 2011), and Baumeister and Peersman (2010),
    Suffix
    among others. In this section, we focus on the structural vector autoregressive model proposed in Kilian and Murphy (2010). This model was designed to help us distinguish, in particular, between unexpected oil production shortfalls, unexpected changes in the global demand for crude oil driven by the global business cycle, and shocks to the demand for above-ground crude oil inventories driven

17
Kilian, L., and B. Hicks (2010), “Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008?” mimeo, Department of Economics, University of Michigan.
Total in-text references: 1
  1. In-text reference with the coordinate start=57148
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    Figure 2 illustrates that much of the surge in the real price of oil between 2003 and mid-2008 was caused by repeated unexpected increases in the global business cycle as opposed to positive speculative demand shocks or negative oil supply shocks. This finding is also consistent with independent evidence on revisions to professional real-GDP growth forecasts for the largest economies in
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    Kilian and Hicks (2010).
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    Kilian and Hicks documented that these forecast surprises were associated with unexpected growth in emerging Asia. As financial markets collapsed in the second half of 2008, so did global real activity and hence demand for industrial commodities such as crude oil.

18
Kilian, L., and D. Murphy (2010), “The Role of Inventories and Speculative Trading in the Global Market for Crude Oil,” mimeo, University of Michigan.
Total in-text references: 12
  1. In-text reference with the coordinate start=20852
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    Consistent series for OECD petroleum stocks are not available prior to 1987.12. We therefore extrapolate the percent change in OECD inventories backwards at the rate of growth of U.S. petroleum inventories, following
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    Kilian and Murphy (2010).
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    For the post-1987.12 period, U.S. and OECD petroleum inventory growth rates are highly correlated. We also adjust the realtime OECD petroleum inventory data to account for changes in the set of OECD members reporting inventories in December 2001.

  2. In-text reference with the coordinate start=22266
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    real-time CPI data from the Federal Reserve Bank of Philadelphia, exploiting the fact that observations shown in each vintage represent the data that were available in the middle of the quarter. 2.4. Data Transformations The real price of oil is constructed by deflating the nominal price of oil by the U.S. consumer price index. World oil production is expressed in growth rates. Following
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    Kilian and Murphy (2010),
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    a proxy for the change in world crude oil inventories is constructed by scaling U.S. crude oil inventories by the ratio of OECD over U.S. petroleum inventories. This approximation is required because there are no monthly crude oil inventory data for other countries.

  3. In-text reference with the coordinate start=27762
    Prefix
    Regression-Based Forecasts Table 1 summarizes the real-time forecast accuracy of ARMA and AR models of the real U.S. refiners’ acquisition cost for crude oil imports and of the four-variable VAR oil market model of
    Exact
    Kilian and Murphy (2010).
    Suffix
    The VAR model includes the percent change in global crude oil production, the Kilian (2009) measure of global real activity (in deviations from trend), the change in global crude oil inventories and the real U.

  4. In-text reference with the coordinate start=32913
    Prefix
    This forecasting success is perhaps unexpected given the common view that the price of oil should be viewed as an asset price and like all asset prices is inherently unpredictable. Our findings are fully consistent, however, with recent work showing that the price of oil most of the time contains only a small asset price component (see
    Exact
    Kilian and Murphy 2010; Kilian and Vega 2011).
    Suffix
    It is also consistent with evidence that the nominal WTI price of oil is predictable based on economic fundamentals at short horizons (see Alquist, Kilian, and Vigfusson 2011). Although we report tests of statistical significance for the success ratios in Table 1b and 2b, we do not provide measures of statistical significance for the MSPE reductions in Tables 1a and 2a.

  5. In-text reference with the coordinate start=47088
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    To the extent that industrial commodity prices are driven by the same fluctuations in global real economic activity as the price of oil, one would expect them to be a good proxy for one of the key predictors included in the VAR model of
    Exact
    Kilian and Murphy (2010).
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    For the same reason, it is no surprise that the forecast accuracy of the model based on industrial commodity prices further improves when using ex-post revised data. One obvious limitation of forecasting models based on non-oil industrial commodity prices is that they can be expected to perform well as long as the real price of oil is driven by the global business cycle as opposed to geopoli

  6. In-text reference with the coordinate start=49032
    Prefix
    in exploring the possible consequences of civil unrest in Libya, or in exploring how much a period of unexpectedly low global demand for crude oil caused by a global recession would lower the real price of oil. The construction of such forecast scenarios requires the use of structural econometric models. Structural models of the global market for crude oil have recently been developed by
    Exact
    Kilian (2009), Kilian and Murphy (2010, 2011), and Baumeister and Peersman (2010),
    Suffix
    among others. In this section, we focus on the structural vector autoregressive model proposed in Kilian and Murphy (2010). This model was designed to help us distinguish, in particular, between unexpected oil production shortfalls, unexpected changes in the global demand for crude oil driven by the global business cycle, and shocks to the demand for above-ground crude oil inventories driven

  7. In-text reference with the coordinate start=49212
    Prefix
    Structural models of the global market for crude oil have recently been developed by Kilian (2009), Kilian and Murphy (2010, 2011), and Baumeister and Peersman (2010), among others. In this section, we focus on the structural vector autoregressive model proposed in
    Exact
    Kilian and Murphy (2010).
    Suffix
    This model was designed to help us distinguish, in particular, between unexpected oil production shortfalls, unexpected changes in the global demand for crude oil driven by the global business cycle, and shocks to the demand for above-ground crude oil inventories driven by speculative motives.

  8. In-text reference with the coordinate start=51166
    Prefix
    Indeed, it can be shown that the structural shocks identified by this model are fundamental in that the oil futures spread does not Granger-cause the other variables included in the model (see Giannone and Reichlin 2006). The reduced-form representation of the
    Exact
    Kilian and Murphy (2010)
    Suffix
    model corresponds to the four-variable VAR model we already considered in sections 2 and 3 and showed to be quite accurate compared with the no-change benchmark as well as competing models. Conditional forecasts may be constructed from the structural moving average representation of the VAR model also used for the historical decomposition by feeding in sequences of future oil demand and oil s

  9. In-text reference with the coordinate start=54468
    Prefix
    We begin in section 5.1 with a brief structural analysis of the sources of fluctuations in the real price of oil since the late 1970s and especially in recent years. 5.1. Historical Decompositions Our analysis updates that in
    Exact
    Kilian and Murphy (2010)
    Suffix
    from 2009.8 until 2010.6, the last date for which we have available ex-post revised data. Although the model is only set-identified, all admissible models can be shown to be quite similar, allowing us to focus on one such model with little loss of generality.

  10. In-text reference with the coordinate start=56634
    Prefix
    In contrast, the decline in the real price of oil in 1998, for example, can be attributed primarily to an unexpected decline in flow demand for crude oil following the Asian crisis. These and other episodes have already been discussed in
    Exact
    Kilian and Murphy (2010).
    Suffix
    Our focus in this paper is on the additional evidence for the aftermath of the financial crisis. Figure 2 illustrates that much of the surge in the real price of oil between 2003 and mid-2008 was caused by repeated unexpected increases in the global business cycle as opposed to positive speculative demand shocks or negative oil supply shocks.

  11. In-text reference with the coordinate start=63352
    Prefix
    such fears could be arbitrarily weak or strong, making it difficult to assess the quantitative importance of this channel, but the historical experience of earlier episodes in Figure 2 provides some guidance. One contagion scenario can be motivated by focusing on the surge in speculative demand that occurred preceding and following the invasion of Kuwait in August of 1990. As discussed in
    Exact
    Kilian (2008) and Kilian and Murphy (2010),
    Suffix
    among others, the invasion not only caused oil production in Kuwait and Iraq to cease, but raised concerns that Saudi Arabia and its smaller neighbors would be invaded next, causing a surge in speculative demand that only subsided after the U.

  12. In-text reference with the coordinate start=72615
    Prefix
    Clearly, forecast scenarios could alternatively be constructed from DSGE models, provided that these models incorporate suitable structural oil market models. One reason for focusing on the model in
    Exact
    Kilian and Murphy (2010)
    Suffix
    instead is 7 Note that this sample period does not include the period of Libyan unrest and turmoil in oil markets that started in February 2011. that currently available DSGE models are still rather simplistic when it comes to modeling the global oil market to be useful for policy analysis.

19
Kilian, L., and D. Murphy (2011), “Why Agnostic Sign Restrictions Are Not Enough: Understanding the Dynamics of Oil Market VAR Models,” forthcoming: Journal of the European Economic Association.
Total in-text references: 1
  1. In-text reference with the coordinate start=49032
    Prefix
    in exploring the possible consequences of civil unrest in Libya, or in exploring how much a period of unexpectedly low global demand for crude oil caused by a global recession would lower the real price of oil. The construction of such forecast scenarios requires the use of structural econometric models. Structural models of the global market for crude oil have recently been developed by
    Exact
    Kilian (2009), Kilian and Murphy (2010, 2011), and Baumeister and Peersman (2010),
    Suffix
    among others. In this section, we focus on the structural vector autoregressive model proposed in Kilian and Murphy (2010). This model was designed to help us distinguish, in particular, between unexpected oil production shortfalls, unexpected changes in the global demand for crude oil driven by the global business cycle, and shocks to the demand for above-ground crude oil inventories driven

20
Kilian, L., and C. Vega (2011), “Do Energy Prices Respond to U.S. Macroeconomic News? A Test of the Hypothesis of Predetermined Energy Prices,” Review of Economics and Statistics, 93, 660-671.
Total in-text references: 1
  1. In-text reference with the coordinate start=32913
    Prefix
    This forecasting success is perhaps unexpected given the common view that the price of oil should be viewed as an asset price and like all asset prices is inherently unpredictable. Our findings are fully consistent, however, with recent work showing that the price of oil most of the time contains only a small asset price component (see
    Exact
    Kilian and Murphy 2010; Kilian and Vega 2011).
    Suffix
    It is also consistent with evidence that the nominal WTI price of oil is predictable based on economic fundamentals at short horizons (see Alquist, Kilian, and Vigfusson 2011). Although we report tests of statistical significance for the success ratios in Table 1b and 2b, we do not provide measures of statistical significance for the MSPE reductions in Tables 1a and 2a.

22
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: 1
  1. In-text reference with the coordinate start=20268
    Prefix
    In constructing the monthly U.S. refiners’ acquisition cost for crude oil imports a further complication arises because these data are only available starting in 1974.1. We followed the procedure outlined in
    Exact
    Mork (1989,
    Suffix
    p. 741) for extrapolating the refiners’ acquisition cost backwards to 1973.1. This procedure involves scaling the monthly percent rate of change in the U.S. crude oil producer price index for 1973.1-1974.1 by the ratio of the growth rate in the annual refiners’ acquisition cost over the growth rate in the annual U.

23
Pesaran, M.H., and A. Timmermann (1995), “Predictability of Stock Returns: Robustness and Economic Significance,” Journal of Finance, 50, 1201-1228.
Total in-text references: 1
  1. In-text reference with the coordinate start=8252
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    In addition, these VAR models have consistently and often significantly higher directional accuracy with success ratios as high as 65% in real time in some cases. Such success ratios are high by the standards of the empirical finance literature (see, e.g.,
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    Pesaran and Timmermann 1995).
    Suffix
    Section 3 also contrasts the real-time forecasting results with results for the same forecasting models based on ex-post revised data. It is not obvious ex ante whether models based on ex-post revised data should forecast more accurately than models based on real-time data.

25
Ravazzolo, F., and P. Rothman (2010), “Oil and U.S. GDP: A Real-Time Out-of-Sample Examination,” mimeo, Norges Bank.
Total in-text references: 1
  1. In-text reference with the coordinate start=11853
    Prefix
    These tools are designed to allow end-users to interpret oil price forecasts in light of economic models and to evaluate their sensitivity to alternative assumptions. The concluding remarks are in section 6. 2. The Real-Time Data Set Unlike
    Exact
    Carlton (2010) and Ravazzolo and Rothman (2010)
    Suffix
    our focus is not on real-time forecasting of U.S. real GDP growth on the basis of lagged oil prices, but rather on generating real-time forecasts of the real price of oil. Our analysis is more closely related to the recent literature on real-time forecasts of the nominal price of oil (see, e.g.

26
Waggoner, D.F., and T. Zha (1999), “Conditional Forecasts in Dynamic Multivariate Models,” Review of Economics and Statistics, 81, 639-651.
Total in-text references: 1
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    An equally important limitation is that such forecasting methods do not convey how sensitive the real oil price forecasts are to hypothetical events in the global market for crude oil. Forecasts that condition on such hypothetical events can be viewed as conditional forecasts (see, e.g.,
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    Waggoner and Zha 1999).
    Suffix
    They are also known as forecast scenarios. For example, a user may be interested in how a temporary oil production shortfall would affect the forecast of the real price of oil. Similarly, one may be interested in exploring the possible consequences of civil unrest in Libya, or in exploring how much a period of unexpectedly low global demand for crude oil caused by a global recession would lowe