Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238791 
Year of Publication: 
2010
Citation: 
[Journal:] International Econometric Review (IER) [ISSN:] 1308-8815 [Volume:] 2 [Issue:] 2 [Publisher:] Econometric Research Association (ERA) [Place:] Ankara [Year:] 2010 [Pages:] 57-72
Publisher: 
Econometric Research Association (ERA), Ankara
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 (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.
Subjects: 
Autoregressive Process
Lag Order Determination
Model Selection Criteria
Cross Validation
JEL: 
C13
C15
C22
C52
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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