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Model Selection and Paradoxes of Prediction

Quantile Journal, Vol. 1, pp. 43-51, 2006

Posted: 20 Oct 2006  

Oleg Itskhoki

Princeton University - Department of Economics

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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.

Notes: Downloadable document is in Russian.

Suggested Citation

Itskhoki, Oleg, Model Selection and Paradoxes of Prediction. Quantile Journal, Vol. 1, pp. 43-51, 2006. Available at SSRN: https://ssrn.com/abstract=938735

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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