Model selection and paradoxes of prediction (in Russian)
AbstractIn 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.
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Bibliographic InfoArticle provided by Quantile in its journal Quantile.
Volume (Year): (2006)
Issue (Month): 1 (September)
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Web page: http://quantile.ru/
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