VARIABLES ESTIMATORS OF A DYNAMIC LINEAR-MODEL

Using a dynamic linear equation that has a conditionally homoscedastic moving average disturbance, we compare two parameterizations of a com monly used instrumental variables estimator to one that is asymptotica lly optimal in a class of estimators that includes the conventional on e. We find that, for some plausible data-generating processes, the opt imal one is distinctly more efficient asymptotically. Simulations indi cate that in samples of size typically available, asymptotic theory de scribes the distribution of the parameter estimates reasonably well bu t that test statistics sometimes are poorly sized.