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A New Test In A Predictive Regression with Structural Breaks

Author

Listed:
  • Zongwu Cai

    (Department of Economics, The University of Kansas)

  • Seong Yeon Chang

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China)

Abstract

This paper considers predictive regressions where a structural break is allowed at some unknown date. We establish novel testing procedures for testing predictability via empirical likelihood methods based on some weighted score equations. Theoretical results are useful in practice because we adopt a unified framework under which it is unnecessary to distinguish whether the predictor variables are stationary or nonstationary. In particular, nonstationary predictor variables are allowed to be (nearly) integrated or exhibit a structural change at some unknown date. Simulations show that the empirical likelihood-based tests perform well in terms of size and power in finite samples. As an empirical analysis, we test asset returns predictability using various predictor variables.

Suggested Citation

  • Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    • Handle: RePEc:kan:wpaper:201811
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    More about this item

    Keywords

    Autoregressive process; Empirical likelihood; Structural change; Unit root; Weighted estimation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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