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Estimation of Dynamic Panel Data Models with Cross‐Sectional Dependence: Using Cluster Dependence for Efficiency
Author(s) -
Verdier Valentin
Publication year - 2015
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2486
Subject(s) - estimator , econometrics , panel data , estimation , cross sectional data , statistics , cluster (spacecraft) , sample (material) , value (mathematics) , mathematics , computer science , economics , physics , management , thermodynamics , programming language
Summary This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross‐sectional dependence. A new estimator is defined that uses cross‐sectional dependence for efficiency while being robust to the misspecification of the form of the cross‐sectional dependence. We show that using cross‐sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses nuisance parameters parsimoniously so that it exhibits good small‐ and large‐sample properties even when the number of time periods is large. As an empirical application, we estimate the effect of attending private school on student achievement using a value‐added model. Copyright © 2015 John Wiley & Sons, Ltd.