This study describes a new Stata routine that computes bias corrected LSDV estimators and their bootstrap variance-covariance matrix for dynamic (possibly) unbalanced panel data models. 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.
Estimation and inference in dynamic unbalanced panel data models with a small number of individuals
BRUNO, GIOVANNI
2005
Abstract
This study describes a new Stata routine that computes bias corrected LSDV estimators and their bootstrap variance-covariance matrix for dynamic (possibly) unbalanced panel data models. 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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