Likelihood-based Estimation of Dynamic Panels with Predetermined Regressors∗

This paper discusses likelihood-based estimation of panel data models with individualspecific effects and both lagged dependent variable regressors and additional predetermined explanatory variables. The resulting new estimator, labeled as sub-system LIML (ssLIML), is asymptotically equivalent to standard panel GMM as N →∞ for fixed T , but tends to present smaller biases in finite samples as illustrated in simulations experiments. Simulation results also indicate that the estimator is preferred to other alternatives available in the literature in terms of finite-sample performance. Finally, as an empirical illustration, I revisit the evidence on the relationship between income and democracy in a panel of countries using the proposed estimator. JEL Codes: C23, D72