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Quantile cointegration in the autoregressive distributed-lag modeling framework

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  • Cho, Jin Seo
  • Kim, Tae-hwan
  • Shin, Yongcheol

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 analyzing 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. The main utilities of this analysis are demonstrated through the empirical application to the dividend policy in the US.

Suggested Citation

  • Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
    • Handle: RePEc:eee:econom:v:188:y:2015:i:1:p:281-300
    DOI: 10.1016/j.jeconom.2015.05.003
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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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