Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals
Giovanni S. F. Bruno
Istituto di Economia Politica, Bocconi University, Milan
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Abstract. This article describes a new Stata routine, xtlsdvc, that computes
bias-corrected least-squares dummy variable (LSDV) estimators and their
bootstrap variance–covariance matrix for dynamic (possibly) unbalanced
panel-data models with strictly exogenous regressors. A Monte Carlo analysis
is carried out to evaluate the finite-sample performance of the
bias-corrected LSDV estimators in comparison to the original LSDV estimator
and three popular N-consistent estimators: Arellano–Bond, Anderson–Hsiao
and Blundell–Bond. Results strongly support the bias-corrected LSDV
estimators according to bias and root mean squared error criteria when the
number of individuals is small.
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Giovanni S. F. Bruno
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xtlsdvc, bias approximation, unbalanced panels, dynamic panel data, LSDV estimator, Monte Carlo experiment, bootstrap variance–covariance
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