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

Author

Listed:
  • Li Tao

    (Renmin University of China)

  • Yuanjie Zhang

    (Renmin University of China)

  • Maozai Tian

    (Renmin University of China
    Lanzhou University of Finance and Economics
    Xinjiang University of Finance and Economics)

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.

Suggested Citation

  • Li Tao & Yuanjie Zhang & Maozai Tian, 2019. "Quantile Regression for Dynamic Panel Data Using Hausman–Taylor Instrumental Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1033-1069, March.
    • Handle: RePEc:kap:compec:v:53:y:2019:i:3:d:10.1007_s10614-017-9779-0
    DOI: 10.1007/s10614-017-9779-0
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    References listed on IDEAS

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    1. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
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    6. Harding, Matthew & Lamarche, Carlos, 2014. "A Hausman–Taylor instrumental variable approach to the penalized estimation of quantile panel models," Economics Letters, Elsevier, vol. 124(2), pages 176-179.
    7. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    8. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
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    Cited by:

    1. Danqing Chen & Jianbao Chen & Shuangshuang Li, 2021. "Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects," Mathematics, MDPI, vol. 9(24), pages 1-24, December.
    2. Ai-bing Ji & Qing-qing Li & Jin-jin Zhang, 2024. "Panel Interval-Valued Data Nonlinear Regression Models and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2413-2435, October.

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