Evaluating Automatic Model Selection

Jennifer Castle, David Hendry and Jurgen Doornik

No 474, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: We evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a constant model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors (N < T) where T is the sample size, then evaluated in simulation experiments for N = 1000. Comparisons with 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, and evaluate Autometrics'capability in dynamic models by its cost of search versus costs of inference.

Keywords: Model selection; Autometrics; Post-selection bias correction; Costs of search; Costs of inference (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
Date: 2010-01-01
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Citations: View citations in EconPapers (5)

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Journal Article: Evaluating Automatic Model Selection (2011)
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