Estimation and Inference in Dynamic Unbalanced Panel-data Models with a Small Number of Individuals

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697 citations0/0 files storedThe Stata Journal Promoting communications on statistics and Stata

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Spatial and Panel Data Analysis Statistical Methods and Bayesian Inference Statistical and numerical algorithms

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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References

1991Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations 1998Initial conditions and moment restrictions in dynamic panel data models 1995Initial conditions and moment restrictions in dynamic panel data models 1981Biases in Dynamic Models with Fixed Effects 1982Formulation and estimation of dynamic models using panel data 1999Estimating dynamic panel data models: a guide for macroeconomists 1995On bias, inconsistency, and efficiency of various estimators in dynamic panel data models 2002Dynamic panel data models: a guide to microdata methods and practice

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