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Tests of Conditional Predictive Ability

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  • Raffaella Giacomini

    (Boston College)

  • Halbert White

    (University of California, San Diego)

Abstract

We argue that the current framework for predictive ability testing (e.g., West, 1996) is not necessarily useful for real-time forecast selection, i.e., for assessing which of two competing forecasting methods will perform better in the future. We propose an alternative framework for out-of-sample comparison of predictive ability which delivers more practically relevant conclusions. Our approach is based on inference about conditional expectations of forecasts and forecast errors rather than the unconditional expectations that are the focus of the existing literature. Compared to previous approaches, our tests are valid under more general data assumptions (heterogeneity rather than stationarity) and estimation methods, and they can handle comparison of both nested and non-nested models, which is not currently possible.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0308001.

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Length: 40 pages
Date of creation: 11 Aug 2003
Date of revision:
Handle: RePEc:wpa:wuwpem:0308001

Note: Type of Document - pdf; pages: 40; figures: included
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Web page: http://128.118.178.162

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Keywords: Forecast Evaluation; Asymptotic Inference; Parameter-reduction Methods;

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