Quantile Journal, Vol. 1, pp. 43-51, 2006
Posted: 20 Oct 2006
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: 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