Abstract
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 in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identification under unit roots, and show that random effects estimation in levels may achieve substantial efficiency gains relative to estimation from data in differences. In an empirical application, we find evidence against unit roots in individual earnings processes from the Panel Study of Income Dynamics and the Spanish section of the European Community Household Panel.
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