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Quantile regression for general spatial panel data models with fixed effects.

Xiaowen Dai1, Zhen Yan2, Maozai Tian3,4,5

  • 1School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, People's Republic of China.

Journal of Applied Statistics
|June 16, 2022
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Summary
This summary is machine-generated.

This study introduces a new quantile regression model for spatial panel data, accounting for individual and time effects. The proposed instrumental variable method offers robust estimation for analyzing complex economic relationships.

Keywords:
60G4262G0562G20Fixed effectsinstrumental variablesquantile regressionspace–time panel modelsspatial autoregressive

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Area of Science:

  • Econometrics
  • Spatial Statistics
  • Panel Data Analysis

Background:

  • Standard regression models may not capture the full distribution of outcomes.
  • Spatial panel data presents unique challenges due to individual and time-specific unobserved heterogeneity.
  • Existing methods may struggle with endogeneity in spatial quantile regression.

Purpose of the Study:

  • To develop a novel quantile regression framework for spatial panel data.
  • To incorporate both individual fixed effects and time period effects.
  • To propose robust estimators using instrumental variables.

Main Methods:

  • Development of fixed effects quantile regression estimators.
  • Application of instrumental variable techniques for endogeneity.
  • Theoretical analysis of asymptotic properties for proposed estimators.
  • Monte Carlo simulations to assess estimator performance.

Main Results:

  • The proposed instrumental variable method provides consistent and efficient estimation.
  • Simulations demonstrate the effectiveness of the new estimators under various scenarios.
  • The methodology is illustrated with a practical application to cigarette demand.

Conclusions:

  • The new quantile regression approach effectively handles spatial dependence and unobserved heterogeneity.
  • The instrumental variable strategy successfully addresses endogeneity issues.
  • This method offers a valuable tool for empirical research in econometrics and related fields.