Title and Abstract
Asymptotically efficient method of moments estimators for dynamic panel data models
In this paper, simple variants of bias correction for dynamic panel data models are proposed and their asymptotic properties are studied when the number of time periods is fixed or tends to infinity with the number of panel units. Our approach can easily be generalized to higher-order autoregressive models and cross-sectional dependence. Panel-corrected standard errors are proposed that allow for robust inference in dynamic models with cross-sectionally correlated errors. Monte Carlo experiments suggest that the bias-corrected method of moment estimator outperforms popular GMM estimators in terms of efficiency and correctly sized tests.
Breitung, J., K. Hayakawa, and S. Kripfganz (2019). Asymptotically efficient method of moments estimators for dynamic panel data models.
Working Paper, University of Cologne.
University of Cologne
University of Exeter