Variance Estimates and Model Selection

Sýdýka Baþçý, Asad Zaman and Arzdar Kiracý
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Sýdýka Baþçý: SESRIC
Arzdar Kiracý: Baþkent University
Authors registered in the RePEc Author Service: Sıdıka Başçı and Arzdar Kiraci

International Econometric Review (IER), 2010, vol. 2, issue 2, 57-72

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.

Keywords: Autoregressive Process; Lag Order Determination; Model Selection Criteria; Cross Validation (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 C52 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Variance Estimates and Model Selection (1998)
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Persistent link: https://EconPapers.repec.org/RePEc:erh:journl:v:2:y:2010:i:2:p:57-72

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