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Time-specific average estimation of dynamic panel regressions

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

Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 4, 581-616

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.

Keywords: first difference least squares (FDLS); fixed effects; panel autoregression; pseudo-panel data; time-specific average (TSA) (search for similar items in EconPapers)
JEL-codes: C22 C23 C33 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/snde-2019-0084

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