Quantile cointegration in the autoregressive distributed-lag modeling framework

Jin Seo Cho (), Tae-Hwan Kim () and Yongcheol Shin

Journal of Econometrics, 2015, vol. 188, issue 1, 281-300

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.

Keywords: QARDL; Quantile regression; Long-run cointegrating relationship; Dividend smoothing; Time-varying rolling estimation (search for similar items in EconPapers)
JEL-codes: C22 G35 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (172)

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Working Paper: Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:188:y:2015:i:1:p:281-300

DOI: 10.1016/j.jeconom.2015.05.003

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