Small sample efficiency gains from a first observation correction for hatanakafs estimator of the lagged dependent variable-serial correlation regression model

Evidence presented by Fomby and Guilkey (1983) suggests that Hatanaka's estimator of the coefficients in the lagged dependent variable-serial correlation regression model performs poorly, not because of poor selection of the estimate of the autocorrelation coefficient, but because of the lack of a first observation correction. This study conducts a Monte Carlo investigationof the small sample efficiency gains obtainable from a first observation correction suggested by Harvey (1981). Results presented here indicate that substantial gains result from the first observation correction. However, in comparing Hatanaka's procedure with first observation correction to maximum likelihood search, it appears that ignoring the determinantal term of the full likelihood function causes some loss of small sample efficiency. Thus, when computer costsand programming constraints are not binding, maximum likelihood search is to be recommended. In contrast, users who have access to only rudimentary least squares programs would be well served when using Hatanaka's two-step procedure with first