Abstract

http://ssrn.com/abstract=1689767
 


 



Variance Estimates and Model Selection


Asad Zaman


Pakistan Institute of Development Economics

Sidika Basci


ESTIM Forecasting Center

Arzdar Kiraci


Baskent University

October 8, 2010

International Econometric Review. Vol. 2, No. 2, September 2010

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.

Number of Pages in PDF File: 17

Keywords: Autoregressive Process, Lag Order Determination, Model Selection Criteria, Cross Validation

JEL Classification: C13, C15, C22, C52

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Date posted: October 10, 2010  

Suggested Citation

Zaman, Asad and Basci, Sidika and Kiraci, Arzdar, Variance Estimates and Model Selection (October 8, 2010). International Econometric Review. Vol. 2, No. 2, September 2010. Available at SSRN: http://ssrn.com/abstract=1689767

Contact Information

Asad Zaman (Contact Author)
Pakistan Institute of Development Economics ( email )
P.O.Box 1091
Islamabad 44000, Federal Capital
Pakistan
Sidika Basci
ESTIM Forecasting Center ( email )
Sairler sok. 32/C
Gaziosmanpasa 06700
Turkey
Arzdar Kiraci
Baskent University ( email )
Turkey
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