Amemiya‘s generalized least squares and tests of overidentification in simultaneous equation models with qualitative or limited dependent variables
指出Amemiya广义最小二乘法不仅能高效估计含定性或受限因变量的联立方程模型，还能顺便得到过度识别约束的检验统计量，对模型评估有价值。
Abstract Amemiya's generalized least squares method for the estimation of simultaneous equation modeis with qualitative or limited dependent variables is known to be efficient relative to many popular two stage estimators. This note points out that test statistics for overidentification restrictions can be obtained as by-products of Amerniya's generalized least squares procedure. Amemiya's procedure is shown to be a minimum chisquare method. The Amemiya procedure is valuable both for efficient estimation and for model evaluation of such models. Keywords: Key Words And Phrases: Amemiya's GlsMinimum Distance MethodMinimum Chi-SquareSimultaneous Equation ModelsQualrtative Dependent VariablesLimited Dependent VariablesOveridentification Tests