Bias-corrected estimation of linear dynamic panel-data models

I present a computationally simple bias-corrected estimator with attractive finite-sample properties, which is implemented in the new xtdpdbcStata package. The estimator relies neither on instrumental variables nor on specific assumptions about the initial observations. Because it is a method of moments estimator, standard errors are readily available from asymptotic theory. Higher-order lags of the dependent variable can be accommodated as well. A useful test for the correct model specification is the Arellano–Bond test for residual 3 autocorrelation. The random-effects versus fixed-effects assumption can be tested using a Hansen overidentification test or a generalized Hausman test. The user can also specify a hybrid model, in which only a subset of the exogenous regressors satisfies a random-effects assumption.