On the Sensitivity of Simultaneous-Equations Estimators to the Stochastic Assumptions of the Models

Abstract

The paper reports the results of several sampling experiments investigating the sensitivity of various simultaneous-equations estimators to errors in the exogenous variables, stochastic coefficients, heteroskedastic disturbances and auto correlated disturbances. The estimates studied are direct least squares, two-stage least squares, Nagar's unbiased k-class estimator, limited-information maximum likelihood, three-stage least squares, and full-information maximum likelihood. Stochastic coefficients altered the ranking of the estimators and increased their dispersions greatly. The other conditions did not produce great changes in the rankings of the estimators, the central tendencies of their distributions or the usefulness of their standard errors.