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State‐dependent evaluation of predictive ability

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  • Boriss Siliverstovs
  • Daniel S. Wochner

Abstract

This study systematically broadens the relevance of possible model performance asymmetries across business cycles in the spirit of the recent state‐dependent forecast evaluation literature (e.g. Chauvet & Potter, 2013) to hundreds of macroeconomic indicators and deepens the forecast evaluation of the recent factor model literature on hundreds of target variables (e.g. Stock & Watson, 2012b) in a state‐dependent manner. Our results are consistent with both strands of the literature and generalize the former to over 200 macroeconomic indicators and differentiate the latter across three levels of temporal granularity: We document systematic model performance differences in both absolute and relative terms across business cycles (longitudinal) as well as across variable groups (cross‐sectional) and find these performance differences to be robust across several alternative specifications. The cross‐sectional prevalence and robustness of state dependency shown in this article encourages economic forecasters to complement model performance assessments with a state‐dependent evaluation of predictive ability.

Suggested Citation

  • Boriss Siliverstovs & Daniel S. Wochner, 2021. "State‐dependent evaluation of predictive ability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 547-574, April.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:3:p:547-574
    DOI: 10.1002/for.2715
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    1. Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.

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