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Forecast combination and the Bank of England’s suite of statistical forecasting models

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  • George Kapetanios
  • Vincent Labhard
  • Simon Price

Abstract

The Bank of England has constructed a ‘suite of statistical forecasting models’ (the ‘Suite’) providing judgement-free statistical forecasts of inflation and output growth as one of many inputs into the forecasting process, and to offer measures of relevant news in the data. The Suite combines a small number of forecasts generated using different sources of information and methodologies. The main combination methods employ weights that are equal or based on the Akaike information criterion (using likelihoods built from estimation errors). This paper sets a general context for this exercise, and describes some features of the Suite as it stood in May 2005. The forecasts are evaluated over the period of Bank independence (1997 Q2 to 2005 Q1) by a mean square error criterion. The forecast combinations generally lead to a reduction in forecast error, although over this period some of the benchmark models are hard to beat.

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Bibliographic Info

Paper provided by Bank of England in its series Bank of England working papers with number 323.

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Date of creation: May 2007
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Handle: RePEc:boe:boeewp:323

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  1. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
  2. Gary Koop & Simon Potter, 2003. "Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging," Discussion Papers in Economics 04/16, Department of Economics, University of Leicester.
  3. Jonathan H. Wright, 2003. "Bayesian Model Averaging and exchange rate forecasts," International Finance Discussion Papers 779, Board of Governors of the Federal Reserve System (U.S.).
  4. Timothy Cogley & Thomas Sargent, . "Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US," Working Papers 2133503, Department of Economics, W. P. Carey School of Business, Arizona State University.
  5. Pesaran, M. H. & Timmermann, A., 1996. "A Recursive Modelling Approach to Predicting UK Stock Returns'," Cambridge Working Papers in Economics 9625, Faculty of Economics, University of Cambridge.
  6. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06.
  7. George Kapetanios & Vincent Labhard & Simon Price, 2006. "Forecasting using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation," Working Papers 566, Queen Mary, University of London, School of Economics and Finance.
  8. Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
  9. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
  10. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  11. Jonathan H. Wright, 2009. "Forecasting US inflation by Bayesian model averaging," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 131-144.
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