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Computational Economics

, Volume 53, Issue 3, pp 1033–1069 | Cite as

Quantile Regression for Dynamic Panel Data Using Hausman–Taylor Instrumental Variables

  • Li Tao
  • Yuanjie Zhang
  • Maozai TianEmail author
Article
  • 419 Downloads

Abstract

This paper considers quantile regression for dynamic fixed effects panel data models with Hausman–Taylor instrumental variables (HTIV). The fixed effects estimators of panel data are typically biased when there existing lagged dependent variables and endogenous covariates as regressors, so we suggest the use of the Hausman–Taylor instrumental variables to reduce the dynamic bias. HTIV can be used even if independent variables do not vary with time when the unobserved heterogeneity is related to the independent variables. Besides, there is no need for HTIV to adapt instrumental variables beyond the model. In this paper, we consider Hausman–Taylor instrumental variables and propose two quantile regression estimators. We study the asymptotic properties of the proposed estimators. Monte Carlo simulation studies are conducted to examine the performance of the two proposed estimators. In addition, we illustrate the new approaches with an application to analyze the factors affecting price of commercialized residential buildings of 35 big and moderate cities in China, finding out that pre-price has a marked effect on current price.

Keywords

Dynamic panel data Quantile regression Fixed effects Penalized regression Hausman–Taylor instrumental variables 

Notes

Acknowledgements

The work was partially supported by the major research projects of philosophy and social science of the Chinese Ministry of Education (No.15JZD015), Project supported by the Major Program of Beijing Philosophy and Social Science Foundation of China (No.15ZDA17), the Key Program of National Philosophy and Social Science Foundation Grant (No.13AZD064), Renmin University of China: the special developing and guiding fund for building world-class universities (disciplines) (No.15XNL008), China Statistical Research Project (No. 2016LD03), the Fund of the Key Research Center of Humanities and Social Sciences in the general Colleges and Universities of Xinjiang Uygur Autonomous Region.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Center for Applied Statistics, School of StatisticsRenmin University of ChinaBeijingChina
  2. 2.School of StatisticsLanzhou University of Finance and EconomicsLanzhouChina
  3. 3.School of Statistics and InformationXinjiang University of Finance and EconomicsÜrümqiChina

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