Evaluating automatic model selection

Working paper

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.

Authors: Castle, J; Hendry, D; Doornik, J

• Publication status: Published
• Publisher: University of Oxford
• Series: Department of Economics Discussion Paper Series
• Publication date: 2010-01-01
• Paper number: 474
• Keywords: post-selection bias correction; autometrics; model selection; costs of inference; costs of search