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

Li Tao, Yuanjie Zhang and Maozai Tian ()
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Li Tao: Renmin University of China
Yuanjie Zhang: Renmin University of China
Maozai Tian: Renmin University of China

Computational Economics, 2019, vol. 53, issue 3, No 7, 1033-1069

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-017-9779-0

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