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Estimation and inference in dynamic unbalanced panel data models with a small number of individuals

Giovanni Bruno ()

No 165, KITeS Working Papers from KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy

Abstract: This study describes a new Stata routine that computes bias-corrected LSDV estimators and thier 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 estimators 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.

Keywords: Bias approximation; Unbalanced panels; Dynamic Panel data; LSDV estimator; Monte Carlo experiment; Bootstrap variance-covariance (search for similar items in EconPapers)
JEL-codes: C23 C15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2005-06, Revised 2005-06
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Related works:
Journal Article: Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals (2005) Downloads
Journal Article: Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals (2005) Downloads
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