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Semiparametric Single-index Predictive Regression

Author

Listed:
  • Weilun Zhou
  • Jiti Gao
  • David Harris
  • Hsein Kew

Abstract

This paper studies a semiparametric single-index predictive regression model with multiple nonstationary predictors that exhibit co-movement behaviour. Orthogonal series expansion is employed to approximate the unknown link function in the model and the estimator is derived from an optimization under constraint. The main finding includes two types of super-consistency rates for the estimators of the index parameter. The central limit theorem is established for a plug-in estimator of the unknown link function. In the empirical studies, we provide ample evidence in favor of nonlinear predictability of the stock return using four pairs of nonstationary predictors.

Suggested Citation

  • Weilun Zhou & Jiti Gao & David Harris & Hsein Kew, 2019. "Semiparametric Single-index Predictive Regression," Monash Econometrics and Business Statistics Working Papers 25/19, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2019-25
    as

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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp25-2019.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    predictive regression; single-index model; Hermite orthogonal estimation; dual super-consistency rates; co-moving predictors.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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