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Classification JEL : Q43, C53, E32
Classification de la Banque : Méthodes économétriques et statistiques; Questions
internationales
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1. Introduction
The realtime nature of data used in forecasting has received increasing attention in recent years
(see, e.g., Clements and Galvão 2010;
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Croushore 2006, 2011).
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Although the real price of oil is
one of the key variables in the modelbased 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.
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This data set allows the
construction of realtime forecasts of the real price of oil from a variety of models. Both
backcasting and nowcasting techniques are used to fill gaps in the realtime 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 realtime data for most series.
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These tools are designed
to allow endusers 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 RealTime Data Set
Unlike
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Carlton (2010) and
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Ravazzolo and Rothman (2010) our focus is not on realtime
forecasting of U.S. real GDP growth on the basis of lagged oil prices, but rather on generating
realtime forecasts of the real price of oil.
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The RealTime Data Set
Unlike Carlton (2010) and Ravazzolo and Rothman (2010) our focus is not on realtime
forecasting of U.S. real GDP growth on the basis of lagged oil prices, but rather on generating
realtime forecasts of the real price of oil. Our analysis is more closely related to the recent
literature on realtime 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 modelbased 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.
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Prior to 1996.1 this publication is not available in electronic form. The
construction of the realtime 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. Realtime data for the monthly U.S. consumer price index are obtained
from the Economic Indicators published by the Council of Economic Advisers.
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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
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Mork (1989,
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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 1973.11974.1 by the ratio of the growth rate in the
annual refiners’ acquisition cost over the growth rate in the annual U.
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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 realtime version of this index of global real activity, the linear deterministic
trend is recursively reestimated in real time.
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RegressionBased Forecasts
Table 1 summarizes the realtime forecast accuracy of ARMA and AR models of the real U.S.
refiners’ acquisition cost for crude oil imports and of the fourvariable 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.
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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;
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Croushore 2011).
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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 realtime data, the construction of which requires further assumptions on
the nature of the data revisions and evidence that these assumptions are met in the realtime data.
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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
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Kilian (2009),
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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 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
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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.
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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
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Kilian (2008) and
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Kilian and Murphy (2010), 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.
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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 realtime 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 realtime forecasting to date has focused on domestic
macroeconomic aggregates. In contrast, our focus in this paper has been on generating realtime
forecasts for the real price of oil, which is widely considered one of the key global
macroeconomic indicators.
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