An Evaluation Of Estimators For Censored Systems Of Equations Using Monte Carlo Simulation

This study makes an empirical comparison of estimators for censored equations using Monte Carlo simulation. The underlying data generation process is rarely known in practice. From the viewpoint of regression, both ordinary censoring rule and sample selection rule are logical rules of censoring. Furthermore, a mixed censoring rule is also possible to govern underlying data generation process. Therefore, it is valuable to examine whether estimators are robust to variations in the assumptions of censoring rules. Five estimators are examined, estimators for ordinary censoring rules include method of simulated scores, Bayesian estimation, and expectation maximization; estimators for sample selection rules include multivariate Heckman two-step method, and Shonkwiler - Yen two-step method. According to our findings, generally a substantial difference exists in the performance of estimators, and hence the choice of estimator appears to be of importance. Apart from difference in performance, estimates from all procedures are reasonably close to estimated parameters.