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Combinación de Proyecciones para el Precio del Petróleo: Aplicación y Evaluación de Metodologías

  • Ercio Muñoz
  • Miguel Ricaurte
  • Mariel Siravegna

This paper conducts an exhaustive out-of-sample forecasting evaluation exercise for the monthly price of crude oil between 1992 and 2011. The idea is to identify the forecasting strategy that results in the “best” forecasts in terms of mean forecasting error. To this end, a wide variety of econometric models as well as future prices are tested for different forecasting horizons in an individual manner, as well as combined. We find that for short horizons (1 and 3 months), an ARIMA specification results in smaller forecasting errors, but for longer horizons (6-24 months), future prices outperform other models. All models are found to underestimate the true price of oil, on average. The combination of these individual models only yields smaller forecasting errors when compared to the “best” individual strategy in a restricted sample ending in 2005. Nevertheless, when we tabulate the number of times one strategy yields the largest forecasting error compared to other alternatives, combinations of forecasts never yields the highest absolute error except one month ahead. These results are robust to the sample selection.

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Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 660.

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Date of creation: Jan 2012
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Handle: RePEc:chb:bcchwp:660
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  1. Kenneth Rogoff & Barbara Rossi & Yu-chin Chen, 2008. "Can Exchange Rates Forecast Commodity Prices?," 2008 Meeting Papers 540, Society for Economic Dynamics.
  2. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  3. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
  4. Radoslaw Stefanski, 2014. "Structural Transformation and the Oil Price," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), pages 484-504, July.
  5. Zivot, Eric & Andrews, Donald W K, 1992. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 251-70, July.
  6. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Working Papers 11-16, Bank of Canada.
  7. Lippi, Francesco & Nobili, Andrea, 2008. "Oil and the Macroeconomy: A Structural VAR Analysis with Sign Restrictions," CEPR Discussion Papers 6830, C.E.P.R. Discussion Papers.
  8. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, 04.
  9. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
  10. Eduardo López E. & Víctor Riquelme P., 2010. "Auge, Colapso y Recuperación de los Precios de Materias Primas entre 2002 y 2010: ¿Qué Hay Detrás?," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(2), pages 129-145, April.
  11. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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