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Power of the Neyman Smooth Test for Evaluating Multivariate Forecast Densities

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  • Jan G. De Gooijer

Abstract

We compare and investigate Neyman's smooth test, its components, and the Kolmogorov-Smirnov (KS) goodness-of-fit test for testing the uniformity of multivariate forecast densities. Simulations indicate that the KS test lacks power when the forecast distributions are misspecified, especially for correlated sequences of random variables. Neyman's smooth test and its components work well in samples of size typically available, although there sometimes are size distortions. The components provide directed diagnosis regarding the kind of departure from the null. For illustration, the tests are applied to forecast densities obtained from a bivariate threshold model fitted to high-frequency financial data.

Suggested Citation

  • Jan G. De Gooijer, 2007. "Power of the Neyman Smooth Test for Evaluating Multivariate Forecast Densities," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(4), pages 371-381.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:371-381
    DOI: 10.1080/02664760701231526
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    Cited by:

    1. Jonas Dovern & Hans Manner, 2020. "Order‐invariant tests for proper calibration of multivariate density forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 440-456, June.
    2. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," VfS Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    3. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.

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