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Time-specific average estimation of dynamic panel regressions.

Ba Chu1

  • 1Department of Economics, Carleton University, B-857 Loeb Building, 1125 Colonel By Drive, Ottawa ON K1S 5B6, Canada.

Studies in Nonlinear Dynamics and Econometrics
|November 7, 2022
PubMed
Summary

This study presents a new, unbiased estimator for panel autoregression models. The method is easy to use and accurate for large datasets, offering correct coefficient estimation.

Keywords:
C22C23C33first difference least squares (FDLS)fixed effectspanel autoregressionpseudo-panel datatime-specific average (TSA)

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Panel data analysis is crucial for understanding dynamic economic relationships.
  • Existing estimators for first-order panel autoregression may suffer from bias, especially with large N and T.

Purpose of the Study:

  • To develop and validate an unbiased least squares estimator for first-order panel autoregression models.
  • To address limitations of existing methods in large N and T settings.

Main Methods:

  • Least squares estimation using time-specific cross-sectional averages.
  • Theoretical derivations of asymptotic unbiasedness.
  • Monte Carlo simulations to assess performance across various N and T combinations.

Main Results:

  • The proposed estimator is asymptotically unbiased.
  • It achieves correct empirical coverage probabilities for model coefficients.
  • Demonstrated feasibility through theoretical analysis and simulations.

Conclusions:

  • The new estimator offers a reliable and implementable solution for panel autoregression.
  • It performs well across diverse panel data dimensions (N and T).
  • Empirical application confirms practical utility.