Evaluating Automatic Model Selection

Jennifer Castle, Jurgen Doornik and David Hendry

Journal of Time Series Econometrics, 2011, vol. 3, issue 1, 33

Abstract: We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, Autometrics (Doornik, 2009), show similar properties, but not restricted to orthogonal cases. Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing as well as evaluate selection in dynamic models by costs of search versus costs of inference.

Keywords: model selection; Autometrics; costs of search; costs of inference (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (79)

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Working Paper: Evaluating Automatic Model Selection (2010)
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DOI: 10.2202/1941-1928.1097

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