• <Source Information> Jin Seo Cho, Tae-Hwan Kim, and Yongcheol Shin (2015): Journal of Econometrics, 188, 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 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.

  • <Source Information> Sangwoo Park (2020): Short-Run Parameter Estimation and Inference on the Quantile Autoregressive Distributed-Lag Model, MA Thesis, Graduate School, Yonsei University, Seoul, Korea (in Korean).

  • <Introduction> The current thesis written in Korean provides program codes written in Matlab for QARDL estimation and inference. For the goal of the thesis, the author reproduces the estimation results and figures contained in the empirical part of Cho, Kim, and Shin (2015). In the thesis, among others, it is noted that the empirical part of Cho, Kim, and Shin (2015) is conducted by estimating the model parameters in a single-step quantile regression, although their QARDL theory presumes estimating the long-run and short-run coefficients separately. The author compares the two estimation procedures using the same data as in Cho, Kim, and Shin (2015) and concludes that the two procedures produce almost identical results. The program codes are provided for readers' interest under the author's consent.