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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.

Suggested Citation

  • John Geweke & Gianni Amisano, 2012. "Prediction with Misspecified Models," American Economic Review, American Economic Association, vol. 102(3), pages 482-486, May.
    • Handle: RePEc:aea:aecrev:v:102:y:2012:i:3:p:482-86
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    References listed on IDEAS

    as
    1. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    2. 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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