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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

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

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Handle: RePEc:boe:boeewp:323

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  1. 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. [Downloadable!]
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  2. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. 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. [Downloadable!]
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  4. Jonathan H. Wright, 2003. "Forecasting U.S. inflation by Bayesian Model Averaging," International Finance Discussion Papers 780, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  5. George Kapetanios & Vincent Labhard & Simon Price, . "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Bank of England working papers 268, Bank of England. [Downloadable!]
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  6. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06. [Downloadable!] (restricted)
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  7. 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. [Downloadable!] (restricted)
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  8. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
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  9. 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.). [Downloadable!]
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  10. 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.). [Downloadable!]
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  11. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-91, January. [Downloadable!] (restricted)
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  1. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
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  2. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," BORRADORES DE ECONOMIA 005273, BANCO DE LA REPÚBLICA. [Downloadable!]
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  3. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute. [Downloadable!]
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  4. Andersson, Michael K & Karlsson, Sune, 2007. "Bayesian Forecast Combination for VAR Models," Working Papers 2007:13, Örebro University, Swedish Business School. [Downloadable!]
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  5. Marie Diron & Benoît Mojon, 2008. "Are inflation targets good inflation forecasts?," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q II, pages 33-45. [Downloadable!]
  6. Marie Diron & Benoît Mojon, 2005. "Forecasting the central bank’s inflation objective is a good rule of thumb," Working Paper Series 564, European Central Bank. [Downloadable!]
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