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Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework

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Listed:
  • JIN SEO CHO

    (Yonsei University)

  • TAE-HWAN KIM

    (Yonsei University)

  • YONGCHEOL SHIN

    (University of York)

Abstract

Xiao (2009) develops a novel estimation technique for quantile cointegrated time series by extending Phillips and Hansen¡¯s (1990) semiparametric approach and Saikkonen¡¯s (1991) parametrically augmented approach. This paper extends Pesaran and Shin¡¯s (1998) autoregressive distributed-lag approach into quantile regression by jointly analysing short-run dynamics and long-run cointegrating relationships across a range of quantiles. We derive the asymptotic theory and provide a general package in which the model can be estimated and tested within and across quantiles. We further affirm our theoretical results by Monte Carlo simulations. Main utilities of this analysis are demonstrated through the empirical application to the dividend policy in the U.S.

Suggested Citation

  • Jin Seo Cho & Tae-Hwan Kim & Yongcheol Shin, 2014. "Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework," Working papers 2014rwp-69, Yonsei University, Yonsei Economics Research Institute.
    • Handle: RePEc:yon:wpaper:2014rwp-69
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    More about this item

    Keywords

    QARDL; Quantile Regression; Long-run Cointegrating Relationship; Dividend Smoothing; Time-varying Rolling Estimation.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G35 - Financial Economics - - Corporate Finance and Governance - - - Payout Policy

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