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Prediction with Misspecified Models

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  • John Geweke
  • Gianni Amisano

Abstract

The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.

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File URL: http://www.aeaweb.org/articles.php?doi=10.1257/aer.102.3.482
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Bibliographic Info

Article provided by American Economic Association in its journal American Economic Review.

Volume (Year): 102 (2012)
Issue (Month): 3 (May)
Pages: 482-86

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Handle: RePEc:aea:aecrev:v:102:y:2012:i:3:p:482-86

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  1. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
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Cited by:
  1. Fawcett, Nicholas & Kapetanios, George & Mitchell, James & Price, Simon, 2014. "Generalised density forecast combinations," Bank of England working papers 492, Bank of England.
  2. Antonio Merlo & Thomas R.Palfrey, 2013. "External Validation of Voter Turnout Models by Concealed Parameter Recovery," PIER Working Paper Archive 13-012, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. Nalan Basturk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute.
  4. Warne, Anders & Coenen, G√ľnter & Christoffel, Kai, 2013. "Predictive likelihood comparisons with DSGE and DSGE-VAR models," Working Paper Series 1536, European Central Bank.

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