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Do Composite Procedures Really Improve the Accuracy of Outlook Forecasts?

  • Colino, Evelyn V.
  • Irwin, Scott H.
  • Garcia, Philip

This paper investigates whether the accuracy of outlook hog price forecasts can be improved using composite forecasts in an out-of-sample context. Price forecasts from four wellrecognized outlook programs are combined with futures-based forecasts, ARIMA, and unrestricted Vector Autoregressive (VAR) models. Quarterly data are available from 1975.I through 2007.IV, which allow for a relatively long out-of-sample evaluation period after permitting model specification and appropriate composite-weight training periods. Results show that futures and numerous composite procedures outperform outlook forecasts. At intermediate horizons, OLS composite procedures perform rather well. The superiority of futures and composite forecasts decreases at longer horizons except for an equal-weighted approach. Importantly, with just few exceptions, nothing outperforms the equal-weight approach significantly in any program or horizon. Overall, findings favor the usage of equal-weighted composites, a result that is consistent with previous empirical findings and recent theoretical papers.

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File URL: http://purl.umn.edu/53052
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Paper provided by NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management in its series 2009 Conference, April 20-21, 2009, St. Louis, Missouri with number 53052.

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Date of creation: Apr 2009
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Handle: RePEc:ags:nccc09:53052
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  1. Todd E. Clark & Michael W. McCracken, 2008. "Averaging forecasts from VARs with uncertain instabilities," Working Papers 2008-030, Federal Reserve Bank of St. Louis.
  2. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2008. "A Panel Data Approach to Economic Forecasting: The Bias-Corrected Average Forecast," Economics Working Papers (Ensaios Economicos da EPGE) 668, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  3. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  4. Allen, P. Geoffrey, 1994. "Economic forecasting in agriculture," International Journal of Forecasting, Elsevier, vol. 10(1), pages 81-135, June.
  5. Diebold, Francis X. & Pauly, Peter, 1990. "The use of prior information in forecast combination," International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
  6. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
  7. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
  8. Evelyn V. Colino & Scott H. Irwin, 2010. "Outlook vs. Futures: Three Decades of Evidence in Hog and Cattle Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 1-15.
  9. Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.
  10. 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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