Abstract

http://ssrn.com/abstract=942870
 
 

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Model Selection and Paradoxes of Prediction


Oleg Itskhoki


Princeton University - Department of Economics

January 18, 2005


Abstract:     
In this essay we postulate a number of theoretical hypotheses allowing one to resolve in some degree the following two prediction paradoxes: (1) why simple linear models often have an advantage in predictive power over more complex nonlinear models that lead to a better in-sample fit; (2) why combinations of forecasts often increase the predictive power of individual forecasts. We also give a numerical example illustrating our theoretical statements.

Number of Pages in PDF File: 10

Keywords: model selection, forecasting, linear and nonlinear models, combination of forecasts

JEL Classification: C10, C22, C53

working papers series


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Date posted: November 7, 2006  

Suggested Citation

Itskhoki, Oleg, Model Selection and Paradoxes of Prediction (January 18, 2005). Available at SSRN: http://ssrn.com/abstract=942870 or http://dx.doi.org/10.2139/ssrn.942870

Contact Information

Oleg Itskhoki (Contact Author)
Princeton University - Department of Economics ( email )
Fisher 306
Princeton, NJ 08544-1021
United States
+1 (609) 258-5493 (Phone)
HOME PAGE: http://www.princeton.edu/~itskhoki
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