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A new semiparametric test for superior predictive ability

Author

Listed:
  • Zongwu Cai
  • Jiancheng Jiang
  • Jingshuang Zhang
  • Xibin Zhang

Abstract

We propose a new method to test the superior predictive ability (SPA) of a benchmark model against a large group of alternative models. The proposed test is useful for reducing potential data snooping bias. Unlike previous methods, we model the covariance matrix by factor models and develop a generalized likelihood ratio (GLR) test statistic for the above testing problem. The GLR test is also extended to a stepwise GLR (step-GLR) test in the spirit of the step-RC test of Romano and Wolf (Econometrica 73(4):1237–1282, 2005 ) and step-SPA test of Hsu et al. (J Empir Financ 17(3):471–484, 2010 ). The step-GLR test can identify the most contributed predictive models to the rejection of the null hypothesis. A Monte Carlo simulation study shows that the GLR test is much more powerful and less conservative than the SPA test of Hansen (J Bus Econ Stat 23(4):365–380, 2005 ). We also present an application to illustrate the use of the GLR test and make a comparison between our GLR and Hansen’s SPA tests. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang & Xibin Zhang, 2015. "A new semiparametric test for superior predictive ability," Empirical Economics, Springer, vol. 48(1), pages 389-405, February.
    • Handle: RePEc:spr:empeco:v:48:y:2015:i:1:p:389-405
    DOI: 10.1007/s00181-014-0887-6
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    References listed on IDEAS

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    More about this item

    Keywords

    Data snooping; Generalized likelihood ratio; Reality check; Technical trading rules; Variance matrix estimation; C14; C53;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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