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The Stata Journal
Volume 5 Number 4: pp. 473-500

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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
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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View all articles with these keywords: xtlsdvc, bias approximation, unbalanced panels, dynamic panel data, LSDV estimator, Monte Carlo experiment, bootstrap variance–covariance

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