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American Economic Review: Vol. 102 No. 3 (May 2012)

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Prediction with Misspecified Models

Article Citation

Geweke, John, and Gianni Amisano. 2012. "Prediction with Misspecified Models." American Economic Review, 102(3): 482-86.

DOI: 10.1257/aer.102.3.482

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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Authors

Geweke, John (Centre for the Study of Choice, U Technology Sydney)
Amisano, Gianni (European Central Bank)

JEL Classifications

C53: Forecasting Methods; Simulation Methods


American Economic Review


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