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A Simple Explanation of the Forecast Combination Puzzle

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  • Jeremy Smith
  • Kenneth F. Wallis

Abstract

This article presents a formal explanation of the forecast combination puzzle, that simple combinations of point forecasts are repeatedly found to outperform sophisticated weighted combinations in empirical applications. The explanation lies in the effect of finite-sample error in estimating the combining weights. A small Monte Carlo study and a reappraisal of an empirical study by Stock and Watson ["Federal Reserve Bank of Richmond Economic Quarterly" (2003) Vol. 89/3, pp. 71-90] support this explanation. The Monte Carlo evidence, together with a large-sample approximation to the variance of the combining weight, also supports the popular recommendation to ignore forecast error covariances in estimating the weight. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2009.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 71 (2009)
Issue (Month): 3 (06)
Pages: 331-355

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Handle: RePEc:bla:obuest:v:71:y:2009:i:3:p:331-355

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Cited by:
  1. Paulo Soares Esteves & António Rua, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  2. Fabian Krueger & Frieder Mokinski & Winfried Pohlmeier, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 63-81, February.
  3. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  4. Masayoshi Hayashi, 2012. "Forecasting Welfare Caseloads: The Case of the Japanese Public Assistance Program," CIRJE F-Series CIRJE-F-846, CIRJE, Faculty of Economics, University of Tokyo.
  5. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2009. "Does forecast combination improve Norges Bank inflation forecasts?," Working Paper 2009/01, Norges Bank.
  6. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, 06.
  7. Anthony Garratt & James Mitchell & Shaun P. Vahey & Elizabeth C. Wakerly, 2009. "Real-time Inflation Forecast Densities from Ensemble Phillips Curves," Birkbeck Working Papers in Economics and Finance 0910, Birkbeck, Department of Economics, Mathematics & Statistics.
  8. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an Optimal Forecast Combination? A Stochastic Dominance Approach to Forecast Combination Puzzle," Working Paper Series 17_12, The Rimini Centre for Economic Analysis.
  9. Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 49-66.
  10. Huiyu Huang & Tae-Hwy Lee, 2006. "To Combine Forecasts or to Combine Information?," Working Papers 200806, University of California at Riverside, Department of Economics, revised Feb 2009.
  11. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), August.
  12. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle," Working Papers 1206, University of Guelph, Department of Economics.

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