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Forecast Combination with Entry and Exit of Experts

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  • Carlos Capistrán
  • Allan Timmermann

Abstract

Combination of forecasts from survey data is complicated by the frequent entry and exit in real time of individual forecasters which renders conventional least squares regression approaches to estimation of the combination weights infeasible. We explore the consequences of this for a variety of forecast combination methods in common use and propose a new method that projects actual outcomes on the equal-weighted forecast as a means of adjusting for biases and noise in the underlying forecasts. Through simulations and an empirical application to inflation forecasts we show that the entry and exit of individual forecasters can have a large effect on the real time performance of conventional forecast combination methods. We also find that the proposed projection on the equal-weighted forecast works well in practice.

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File URL: http://www.banxico.org.mx/publicaciones-y-discursos/publicaciones/documentos-de-investigacion/banxico/%7BDACFF00C-023C-48DF-1E71-91CC2A2CEE6F%7D.pdf
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Bibliographic Info

Paper provided by Banco de México in its series Working Papers with number 2006-08.

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Date of creation: Sep 2006
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Handle: RePEc:bdm:wpaper:2006-08

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Web page: http://www.banxico.org.mx
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Keywords: Forecasting; forecast combination; inflation; surveys;

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References

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  1. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1998. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," CEPR Discussion Papers 1908, C.E.P.R. Discussion Papers.
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Citations

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Cited by:
  1. Frank A.G. den Butter & Pieter W. Jansen, 2008. "Beating the Random Walk: a Performance Assessment of Long-term Interest Rate Forecasts," Tinbergen Institute Discussion Papers 08-102/3, Tinbergen Institute.
  2. Laurent Pauwels & Andrey Vasnev, 2011. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Working Papers 11/2011, University of Sydney Business School, Discipline of Business Analytics, revised Jun 2011.
  3. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
  4. João Valle e Azevedo, 2011. "Rational vs. professional forecasts," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  5. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
  6. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393, April.
  7. 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.
  8. Sancetta, A., 2007. "Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains," Cambridge Working Papers in Economics 0735, Faculty of Economics, University of Cambridge.
  9. Carlos Capistrán & Gabriel López-Moctezuma, 2010. "Forecast Revisions of Mexican Inflation and GDP Growth," Working Papers 2010-11, Banco de México.
  10. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.

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