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Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects
Xiaowen Dai1,2, Libin Jin1,2
1School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
Abstract:
This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.
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