This paper presents a model for the heterogeneity and dynamics of the conditional mean and conditional variance of individual wages. A bias-corrected likelihood approach, which reduces the estimation bias to a term of order 1/T2, is used for estimation and inference. The small-sample performance of the proposed estimator is investigated in a Monte Carlo study. The simulation results show that the bias of the maximum likelihood estimator is substantially corrected for designs calibrated to the data used in the empirical analysis, drawn from the PSID. The empirical results show that it is important to account for individual unobserved heterogeneity and dynamics in the variance, and that the latter is driven by job mobility. The model also explains the non-normality observed in log-wage data.
