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Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework

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Publication details

JournalJournal of Econometrics
DateAccepted/In press - 24 May 2015
DateE-pub ahead of print - 9 Jun 2015
DatePublished (current) - Sep 2015
Issue number1
Number of pages20
Pages (from-to)281-300
Early online date9/06/15
Original languageEnglish


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

    Research areas

  • QARDL, Quantile Regression, Long-run Cointegrating Relationship, Dividend Smoothing, Time-varying Rolling Estimation

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