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161223 ||| eng |
| 020 |
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|a 9781513524276
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| 100 |
1 |
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|a Beckers, Benjamin
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| 245 |
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|a Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?
|c Benjamin Beckers, Samya Beidas-Strom
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2015
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| 300 |
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|a 32 pages
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| 651 |
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4 |
|a United States
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| 653 |
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|a Energy: Demand and Supply
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| 653 |
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|a Institutional Investors
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| 653 |
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|a Oil prices
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| 653 |
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|a Pension Funds
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| 653 |
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|a Investments: Futures
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| 653 |
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|a Finance
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| 653 |
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|a Econometric analysis
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| 653 |
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|a Dynamic Treatment Effect Models
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| 653 |
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|a Oil
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| 653 |
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|a Investments: Energy
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| 653 |
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|a Financial institutions
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| 653 |
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|a Economic Forecasting
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| 653 |
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|a Financial Instruments
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Economic forecasting
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| 653 |
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|a Petroleum industry and trade
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| 653 |
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|a Vector autoregression
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| 653 |
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|a Time-Series Models
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| 653 |
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|a Forecasting
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| 653 |
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|a Derivative securities
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| 653 |
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|a Non-bank Financial Institutions
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| 653 |
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|a Energy: General
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| 653 |
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|a Commodities
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| 653 |
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|a Forecasting and Other Model Applications
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| 653 |
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|a Energy and the Macroeconomy
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| 653 |
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|a Prices
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Dynamic Quantile Regressions
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| 653 |
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|a Econometrics
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| 653 |
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|a Investment & securities
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| 653 |
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|a Econometrics & economic statistics
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| 653 |
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|a State Space Models
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| 653 |
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|a Futures
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| 700 |
1 |
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|a Beidas-Strom, Samya
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
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|a IMF Working Papers
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| 028 |
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|a 10.5089/9781513524276.001
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| 856 |
4 |
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|u https://elibrary.imf.org/view/journals/001/2015/251/001.2015.issue-251-en.xml?cid=43423-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons
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