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

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dc.creatorBruno, Giovanni S. F.
dc.date2017-04-01T14:08:43Z
dc.date.accessioned2026-07-09T05:49:58Z
dc.descriptionThis 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.
dc.identifierOther:st0091
dc.identifierdoi:10.22004/ag.econ.117540
dc.identifierhttps://ageconsearch.umn.edu/record/117540/files/sjart_st0091.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/117540
dc.identifier.urihttp://hdl.handle.net/123456789/568924
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/117540
dc.titleEstimation and inference in dynamic unbalanced panel-data models with a small number of individuals
dc.typeText

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