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

Xiaowen Dai1,2, Libin Jin1,2

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Summary
This summary is machine-generated.

This study introduces a faster quantile regression method for spatial panel data with fixed effects. The new instrumental variable (IV) approach improves computational efficiency for parameter estimation in econometrics.

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Area of Science:

  • Econometrics
  • Spatial Statistics
  • Panel Data Analysis

Background:

  • Quantile regression is crucial for understanding conditional distributions.
  • Spatial panel data models are essential for analyzing geographically referenced data with time-series properties.
  • Existing instrumental variable (IV) fixed effects quantile regression (FEQR) methods can be computationally intensive.

Purpose of the Study:

  • To propose efficient minimum distance quantile regression estimators for spatial panel data with individual fixed effects.
  • To develop an instrumental variable (IV) based method that is computationally faster than existing approaches.
  • To establish the asymptotic properties of the proposed estimators.

Main Methods:

  • Development of minimum distance quantile regression estimators.
  • Application of instrumental variable (IV) methods for parameter estimation.
  • Theoretical analysis of asymptotic properties.
  • Empirical validation through simulation studies.

Main Results:

  • The proposed IV-based minimum distance estimators are computationally efficient.
  • The asymptotic properties of the new estimators are theoretically established.
  • Simulation results demonstrate the effectiveness of the proposed method.

Conclusions:

  • The novel method offers a computationally advantageous alternative for spatial panel data analysis.
  • This research contributes to the advancement of econometric techniques for complex panel data structures.
  • The methodology is applicable to real-world economic datasets, such as cigarette demand analysis.