We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias corrected conditional score estimators, random effects maximum likelihood (RML) in levels and first differences, and estimators that impose mean...

In this paper we derive the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N→ 0 the fixed T results...

We model earnings processes allowing for lots of heterogeneity across agents. We also introduce an extension to the linear ARMA model which allows the initial convergence in the long run to be different from that implied by the conventional ARMA model. This is particularly important for unit...