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Evaluating Direct Multistep Forecasts

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  • Todd Clark
  • Michael McCracken

Abstract

This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy and encompassing applied to direct, multistep predictions from nested regression models. We first derive asymptotic distributions; these nonstandard distributions depend on the parameters of the data-generating process. We then use Monte Carlo simulations to examine finite-sample size and power. Our asymptotic approximation yields good size and power properties for some, but not all, of the tests; a bootstrap works reasonably well for all tests. The paper concludes with a reexamination of the predictive content of capacity utilization for inflation.

Suggested Citation

  • Todd Clark & Michael McCracken, 2005. "Evaluating Direct Multistep Forecasts," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 369-404.
  • Handle: RePEc:taf:emetrv:v:24:y:2005:i:4:p:369-404
    DOI: 10.1080/07474930500405683
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    Keywords

    Causality; Long horizon; Prediction;

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