Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects.

Xiaowen Dai,Libin Jin

doi:10.1371/journal.pone.0261144

Xiaowen Dai, Libin Jin

Open Access

https://doi.org/10.1371/journal.pone.0261144

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Journal: PloS one	Publication Date: Dec 14, 2021
Citations: 2	License type: CC BY 4.0

Affiliation: Shanghai Lixin University of Accounting and Finance

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.

Highlights

In the last few decades, spatial autoregressive (SAR) models have been studied and applied to many areas such as economics, demography, geography and other scientific areas
Dai et al [9] investigated fixed effects quantile regression for general spatial panel data models with both individual fixed effect and time period effects based on instrumental variable method
The problem of bias for quantile regression for spatial autoregressive panel data model can be ameliorated through the use of instrumental variables

Summary

Introduction

In the last few decades, spatial autoregressive (SAR) models have been studied and applied to many areas such as economics, demography, geography and other scientific areas. Dai et al [9] investigated fixed effects quantile regression for general spatial panel data models with both individual fixed effect and time period effects based on instrumental variable method. Bai and Li [11] studied quasi-maximum likelihood estimator of dynamic spatial panel data models with common shocks to deal with both weak and strong cross-sectional correlations. Li and Yang [12] developed M-estimation and inference methods for spatial dynamic panel data models with correlated random effects based on short panels. MDQR for SAR panel data models with fixed effects regression methods. We employ the MDQR methodology for estimating the SAR panel data model with individual fixed effects.

The model with individual fixed effects

The IV-MDQR estimator

Asymptotic theory

Monte Carlo simulations

Illustration

Conclusion

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