Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework

Jin Seo Cho (), Tae-Hwan Kim () and Yongcheol Shin
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Yongcheol Shin: University of York

No 2014rwp-69, Working papers from Yonsei University, Yonsei Economics Research Institute

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.

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)
Pages: 48pages
Date: 2014-11
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

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Journal Article: Quantile cointegration in the autoregressive distributed-lag modeling framework (2015)
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