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Estimation for generalized linear cointegration regression models through composite quantile regression approach

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  • Liu, Bingqi
  • Pang, Tianxiao
  • Cheng, Siang

Abstract

This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.

Suggested Citation

  • Liu, Bingqi & Pang, Tianxiao & Cheng, Siang, 2024. "Estimation for generalized linear cointegration regression models through composite quantile regression approach," Finance Research Letters, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:finlet:v:65:y:2024:i:c:s154461232400597x
    DOI: 10.1016/j.frl.2024.105567
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    References listed on IDEAS

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    More about this item

    Keywords

    Composite quantile regression; Fully modified procedure; Generalized linear cointegration regression model; Portfolio optimization;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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