Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors

This article discusses the likelihood-based estimation of panel data models with individual-specific effects and both lagged dependent variable regressors and additional predetermined explanatory variables. The resulting new estimator, labeled as subsystem limited information maximum likelihood (ssLIML), is asymptotically equivalent to standard panel generalized method of moment as <italic>N</italic> →∞ for fixed <italic>T</italic> but tends to present smaller biases in finite samples as illustrated in simulation experiments. Simulation results also indicate that the estimator is preferred to other alternatives available in the literature in terms of finite-sample performance. Finally, to provide an empirical illustration, I revisit the evidence on the relationship between income and democracy in a panel of countries using the proposed estimator.