Model selection critieria in economic contexts

• Main Author: Fox, Kevin John
• Language: English
• Published: 2009
• Subjects: Econometric models
• Online Access: http://hdl.handle.net/2429/8793

Model selection criteria are used in many contexts in economics. The issue of determining an appropriate criterion, or alternative method, for model selection is a topic of much interest for applied econometricians. These criteria are used when formal testing methods are difficult due to a large number of models being compared, or when a sequential modelling strategy is being used. In econometrics, we are familiar with the use of model selection criteria for determining the order of an ARMA process and the number of dependent variable lags in Augmented Dickey-Fuller equations. The latter application is examined as an interesting example of the sensitivity of results to the choice of criterion. An application of model selection criteria to spline fitting is also considered, introducing a new, flexible, modelling strategy for technical progress in a production economy and for returns to scale in a resource economics context. In this latter context we have a system of estimating equations. Two of the criteria which are compared are the Cross-Validation score (CV) and the Generalized Cross- Validation Criterion (GCV), which until now have only had single equation context expressions. Multiple equation expressions for these criteria are introduced, and are used in the two applications. Comparison of the models selected by the different criteria in each context reveals that results can differ greatly with the choice of criterion. In the unit root test application, the choice of criterion influences the number of times the false hypothesis is not rejected. In the production economy and resource applications, measures of technical progress and returns to scale differ greatly, as do own and cross price elasticities, depending on which criterion is used for selecting the appropriate spline structure. An overview of the literature on model selection is given, with new expressions and interpretations for some model selection criteria, and historical notes.