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Time-specific average estimation of dynamic panel regressions.


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.

SUBMITTER: Chu B 

PROVIDER: S-EPMC9578315 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Time-specific average estimation of dynamic panel regressions.

Chu Ba B  

Studies in nonlinear dynamics and econometrics 20210726 4


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 (<i>N</i>) and the number of time periods (<i>T</i>) can be large, and there is no restriction on the growth rate of <i>N</i> relative to <i>T</i>. It is d  ...[more]

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