Published in: International Econometric Review Vol 2, issue 2. [link to journal]
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co=authored by Siddika Basci and Arzdar Kiraci.
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 (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 (PRESS) in the functions of
several model selection criteria. Such replacements improve the probability of
finding the true model, at least in large samples.
Key words: Autoregressive process, Lag order determination, Model selection
criteria, Cross validation
JEL Classifications: C13, C15, C22, C52