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Model selection and paradoxes of prediction (in Russian)

Author

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  • Oleg Itskhoki

    (Harvard University, USA
    Central Economics & Mathematics Institute, Russia)

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.

Suggested Citation

  • Oleg Itskhoki, 2006. "Model selection and paradoxes of prediction (in Russian)," Quantile, Quantile, issue 1, pages 43-51, September.
    • Handle: RePEc:qnt:quantl:y:2006:i:1:p:43-51
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    References listed on IDEAS

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    1. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    2. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
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    Cited by:

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    2. Roman S. Leukhin, 2019. "Short-Term Fiscal Projections Using Forecast Combination Approach," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 9-21, June.

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