A bias-corrected fixed effects estimator in the dynamic panel data model

Chihwa Kao, Long Liu () and Rui Sun ()
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Long Liu: Florida Atlantic University
Rui Sun: University of Connecticut

Empirical Economics, 2021, vol. 60, issue 1, No 9, 205-225

Abstract: Abstract In this paper, we propose a biased-corrected FE estimator for the dynamic panel data model that works for the autoregressive coefficient $$\rho \in (-1,1]$$ ρ ∈ ( - 1 , 1 ] . We further derive the asymptotic result of the suggested bias-corrected FE estimator. We show that when $$\rho =1$$ ρ = 1 , the suggested estimator is super-consistent and is more efficient than the existing estimators that also work for $$\rho \in (-1,1]$$ ρ ∈ ( - 1 , 1 ] . In addition, when the initial condition is nonstationary, many of the existing dynamic estimators become inconsistent; however, the consistency of the bias-corrected FE estimator we propose does not depend on the stationarity of the initial condition. We also compare the finite sample performances of these estimators using Monte Carlo simulations.

Keywords: Dynamic panel data; Bias-corrected estimator; Fixed effects; Nonstationarity; Super-efficient estimator; Initial condition (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00181-020-01995-0

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