Evaluating Automatic Model Selection

Jan 2010 | 474

Authors: Jennifer Castle,David Hendry, Jurgen A. Doornik

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.

JEL Codes: C51, C22

Keywords: Model selection, Autometrics, Post-selection bias correction, Costs of search, Costs of inference

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