Quantile cointegration in the autoregressive distributed-lag modeling framework

Jin Seo Cho, Tae Hwan Kim, Yongcheol Shin

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number4124
Pages (from-to)281-300
Number of pages20
JournalJournal of Econometrics
Volume188
Issue number1
DOIs
Publication statusPublished - 2015 Sep 1

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Cointegration
Quantile
Time series
Modeling
Quantile Regression
Dividend
Asymptotic Theory
Long-run
Monte Carlo Simulation
Range of data
Framework
Monte Carlo simulation
Distributed lag
Model

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

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Quantile cointegration in the autoregressive distributed-lag modeling framework. / Cho, Jin Seo; Kim, Tae Hwan; Shin, Yongcheol.

In: Journal of Econometrics, Vol. 188, No. 1, 4124, 01.09.2015, p. 281-300.

Research output: Contribution to journalArticle

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