TRUNCATED REGRESSION IN EMPIRICAL ESTIMATION

In this paper we illustrate the use of alternative truncated regression estimators for the general linear model. These include variations of maximum likelihood, Bayesian, and maximum entropy estimators in which the error distributions are doubly truncated. To evaluate the performance of the estimators (e.g., efficiency) for a range of sample sizes, Monte Carlo sampling experiments are performed. We then apply each estimator to a factor demand equation for wheat-by-class.