Variance Estimates and Model Selection

Authors : Sıdıka BAŞÇI, Asad ZAMAN, Arzdar KİRACI

Pages : 57-72

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Publication Date : 2010-09-01

Article Type : Research Paper

Abstract :The large majority of the criteria for model selection are functions of the usual variance estimate for a regression model. The validity of the usual variance estimate depends on some assumptions, most critically the validity of the model being estimated. This is often violated in model selection contexts, where model search takes place over invalid models. A cross validated variance estimate is more robust to specification errors insert ignore into journalissuearticles values(see, for example, Efron, 1983);. We consider the effects of replacing the usual variance estimate by a cross validated variance estimate, namely, the Prediction Sum of Squares insert ignore into journalissuearticles values(PRESS); in the functions of several model selection criteria. Such replacements improve the probability of finding the true model, at least in large samples.
Keywords : Autoregressive Process, Lag Order Determination, Model Selection Criteria, Cross Validation