Forecasting the Price of Oil

Alquist, Ron; Kilian, Lutz; Vigfusson, Robert J.

doi:10.1016/b978-0-444-53683-9.00008-6

Cited by 292 publications

(139 citation statements)

References 72 publications

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“…The second column of Table A1 demonstrates that this alternative specification obtained by imposing the unit root on the ARMA(1,1) process, if anything, has even higher MSPE ratios. This evidence suggests that the process actually is not well characterized as a unit root process or near unit root process, mirroring similar results for the price of crude oil in Alquist et al (). Finally, relaxing the dynamic specification by specifying an ARMA(1,1) model in first differences (ARIMA(1,1)) further increases the MSPE ratios.…”

Section: Forecast Evaluationsupporting

confidence: 76%

“…BAR models are likely to be preferable when working with less parsimonious models with many autoregressive lags. Table A1 shows results based on a fixed lag order of 12, which has also been shown to work well in forecasting the real price of oil (see Alquist et al , ). Further analysis revealed that allowing for larger fixed lag orders or, for that matter, reducing the lag order, does not improve on this baseline model.…”

Section: Forecast Evaluationmentioning

confidence: 96%

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Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump

Baumeister

Kilian

Lee

2016

J of Applied Econometrics

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Summary Appropriate real‐time forecasting models for the US retail price of gasoline yield substantial reductions in the mean‐squared prediction error (MSPE) at horizons up to 2 years as well as substantial increases in directional accuracy. Even greater MSPE reductions are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the US Energy Information Administration gasoline price forecasts and the gasoline price expectations in the Michigan Survey of Consumers. We also show that as much as 39% of the decline in gas prices between June and December 2014 was predictable. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Forecast Evaluationsupporting

confidence: 76%

Section: Forecast Evaluationmentioning

confidence: 96%

Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump

Baumeister

Kilian

Lee

2016

J of Applied Econometrics

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“…Akram (2009), who also finds evidence that a weaker dollar leads to higher commodity prices, and that interest rate reductions cause excessive price increases in oil and industrial raw materials, draws a similar conclusion. Different results are obtained by Frankel and Rose (2010) and Alquist et al (2011), who find no statistically significant relationships between real interest rates and oil prices.…”

Section: Introductionmentioning

confidence: 61%

The Impact of the Euro Area Macroeconomy on Global Commodity Prices

Papież

Śmiech

Dąbrowski

2016

AOC

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The aim of this paper is to analyse the links between real and financial processes in the euro area and energy and non-energy commodity prices. Monthly data spanning 1997:1 to 2013:12 and the structural VAR model are used to uncover the relationship between global commodity prices and the euro area economy. The analysis is performed for three sub-periods in order to capture potential changes in this relationship over time. The main finding is that commodity prices in the euro area do not respond to impulses from production (economic activity), whereas commodity prices strongly react to impulses from financial processes, i.e. interest rates in the euro area and the exchange rate of the dollar against the euro, especially in the run-up to the global financial crisis. The study also provides evidence of a tightening relationship between energy and non-energy commodity prices.

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“…We consider the West Texas Intermediate crude oil prices (WTI) as a proxy for crude oil. Recently, Alquist and Kilian (2010) [15], Alquist et al (2013) [18], Baumeister et al (2013Baumeister et al ( , 2014Baumeister et al ( , 2015 [13,17,19], [12], Xiong et al (2013) [20], Yin and Yang (2016) [21], Drachal (2016) [10] and Naser (2016) [11] all regarded WTI as a proxy variable for oil prices. We selected four factors: supply, demand, crack spread, and non-energy commodity prices.…”

Section: Empirical Results and Robustness Testmentioning

confidence: 99%

Improving the Forecasting Accuracy of Crude Oil Prices

2018

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Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper's method is robust and performs well compared to random walk.

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Forecasting the Price of Oil

Cited by 292 publications

References 72 publications

Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump

Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump

The Impact of the Euro Area Macroeconomy on Global Commodity Prices

Improving the Forecasting Accuracy of Crude Oil Prices

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