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.
Bibliographical noteFunding Information:
We are mostly grateful to the Co-editor, Han Hong, and two anonymous referees for their helpful comments. We are also grateful to Youngsoo Bae, Stephane Bonhomme, Yongsung Chang, Kausik Chaudhuri, Seonghoon Cho, In Choi, Kyungwook Choi, Viet Anh Dang, Ana-Maria Fuertes, Matthew Greenwood-Nimmo, Hwankoo Kang, Dowan Kim, Junhan Kim, Gary Koop, Dongjin Lee, Viet Hoang Ngyuen, Sangsoo Park, Yangsoo Park, Peter Phillips, Kevin Reilly, Xin Shen, Kyulee Shin, Peter Smith, Seungjoo Song, Peter Spencer, Till van Treeck, Mike Wickens, Ralf Wilke, seminar participants at the Bank of Korea, the Universities of Brunel, Korea, Leeds, Melbourne, Seoul, Sogang, Yonsei, York, Cass Business School, and the Institut für Makroökonomie und Konjunkturforschung (IMK, Dusseldorf), and conference delegates at the Conference in Honour of Professor P.C.B. Phillips (University of York, 12–13 July 2012) for their helpful comments. Kim is grateful for financial support from the National Research Foundation of Korea Grant funded by the Korean Government ( NRF-2013S1A3A2053799 ).
© 2015 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Economics and Econometrics