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American Economic Review Vol. 107 No. 11 2017

Gresham's Law of Model Averaging

In-Koo Cho1; Kenneth Kasa2

1 Department of Economics, University of Illinois, Urbana-Champaign, 1407 West Gregory, Urbana, IL 61801, Hanyang University, and Federal Reserve Bank of St. Louis (email: ) · 2 Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada (email: )

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Abstract

A decision maker doubts the stationarity of his environment. In response, he uses two models, one with time-varying parameters, and another with constant parameters. Forecasts are then based on a Bayesian model averaging strategy, which mixes forecasts from the two models. In reality, structural parameters are constant, but the (unknown) true model features expectational feedback, which the reduced-form models neglect. This feedback permits fears of parameter instability to become self-confirming. Within the context of a standard asset-pricing model, we use the tools of large deviations theory to show that even though the constant parameter model would converge to the rational expectations equilibrium if considered in isolation, the mere presence of an unstable alternative drives it out of consideration.

DOI
10.1257/aer.20160665
Volume
107
Issue
11
Pages
3589-3616
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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