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Model Averaging and its Use in Economics

Mark Steel ()

MPRA Paper from University Library of Munich, Germany

Abstract: The method of model averaging has become an important tool to deal with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations, as are common in economics. Model averaging is a natural response to model uncertainty in a Bayesian framework, so most of the paper deals with Bayesian model averaging. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on the problem of variable selection in linear regression models, but the paper also discusses other, more challenging, settings. Some of the applied literature is reviewed with particular emphasis on applications in economics. The role of the prior assumptions in Bayesian procedures is highlighted, and some recommendations for applied users are provided

Keywords: Bayesian methods; Model uncertainty; Normal linear model; Prior specification; Robustness (search for similar items in EconPapers)
JEL-codes: C11 C15 C20 C52 O47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-ore
Date: 2017-09-19
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Working Paper: Model Averaging and its Use in Economics (2018) Downloads
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