The 13 reference contexts in paper Christiane Baumeister, Lutz Kilian (2011) “Real-Time Forecasts of the Real Price of Oil” / RePEc:bca:bocawp:11-16

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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., 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 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.
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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.
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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
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    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.
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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.
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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.
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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 1973.1-1974.1 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 real-time version of this index of global real activity, the linear deterministic trend is recursively re-estimated in real time.
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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.
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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 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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    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 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.
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