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Time-specific average estimation of dynamic panel regressions

Author

Listed:
  • Chu Ba

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

Abstract

This paper introduces an unbiased estimator based on least squares involving time-specific cross-sectional averages for a first-order panel autoregression with a strictly exogenous covariate. The proposed estimator is straightforward to implement as long as the variables of interest have sufficient time variation. The number of cross-sections (N) and the number of time periods (T) can be large, and there is no restriction on the growth rate of N relative to T. It is demonstrated via both theory and a simulation study that the estimator is asymptotically unbiased, and it can provide correct empirical coverage probabilities for the ‘true’ coefficients of the model for various combinations of N and T. An empirical application is also provided to confirm the feasibility of the proposed approach.

Suggested Citation

  • Chu Ba, 2022. "Time-specific average estimation of dynamic panel regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(4), pages 581-616, September.
    • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:4:p:581-616:n:4
    DOI: 10.1515/snde-2019-0084
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    More about this item

    Keywords

    first difference least squares (FDLS); fixed effects; panel autoregression; pseudo-panel data; time-specific average (TSA);
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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