STOCHASTIC PRODUCTION FUNCTION ESTIMATION - SMALL SAMPLE PROPERTIES OF ML VERSUS FGLS

Just-Pope production functions have been traditionally estimated by fe asible generalized least squares (FGLS). This paper investigates the s mall-sample properties of FGLS and maximum likelihood (ML) estimators in heteroscedastic error models. Monte Carlo experiment results show t hat in small samples, even when the error distribution departs signifi cantly from normality, the ML estimator is more efficient and suffers from less bias than FGLS. Importantly, FGLS was found to seriously und erstate the risk effects of inputs and provide biased marginal product estimates. These results are explained by showing that the FGLS crite ria being optimized at the multiple stages are not logically consisten t.