LARGE SAMPLE ASYMPTOTIC PROPERTIES OF ORDINARY LEAST SQUARES, TWO STAGE LEAST SQUARES AND LIMITED INFORMATION MAXIMUM LIKELIHOOD ESTIMATORS IN SIMULTANEOUS EQUATIONS MODELS

2002 ◽  
Author(s):  
R. TIWARI ◽  
V. K. SRIVASTAVA
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Asymptotic Properties ◽  
Maximum Likelihood Estimators ◽  
Ordinary Least Squares ◽  
Simultaneous Equations ◽  
Limited Information ◽  
Two Stage ◽  
Large Sample ◽  
Simultaneous Equations Models

Comparing Single-Equation Estimators in a Simultaneous Equation System

1986 ◽  
Vol 2 (1) ◽  
pp. 1-32 ◽  
Author(s):  
T. W. Anderson ◽  
Naoto Kunitomo ◽  
Kimio Morimune
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Maximum Likelihood Estimator ◽  
Equation System ◽  
Single Equation ◽  
Ordinary Least Squares ◽  
Limited Information ◽  
Likelihood Estimator ◽  
Mean Squared Errors ◽  
Simultaneous Equation System

Comparisons of estimators are made on the basis of their mean squared errors and their concentrations of probability computed by means of asymptotic expansions of their distributions when the disturbance variance tends to zero and alternatively when the sample size increases indefinitely. The estimators include k-class estimators (limited information maximum likelihood, two-stage least squares, and ordinary least squares) and linear combinations of them as well as modifications of the limited information maximum likelihood estimator and several Bayes' estimators. Many inequalities between the asymptotic mean squared errors and concentrations of probability are given. Among medianunbiasedestimators, the limited information maximum likelihood estimator dominates the median-unbiased fixed k-class estimator.

scholarly journals CDSIMEQ: A Program to Implement Two-stage Probit Least Squares

2003 ◽  
Vol 3 (2) ◽  
pp. 157-167 ◽  
Author(s):  
Omar M. G. Keshk
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Least Squares Estimation ◽  
Simultaneous Equations ◽  
The Other ◽  
Endogenous Variable ◽  
Standard Errors ◽  
Two Stage ◽  
Simultaneous Equations Models ◽  
Endogenous Variables

The cdsimeq command implements the two-stage probit least squares estimation method described in Maddala (1983) for simultaneous equations models in which one of the endogenous variables is continuous and the other endogenous variable is dichotomous.1 The cdsimeq command implements all the necessary procedures for obtaining consistent estimates for the coefficients, as well as their corrected standard errors.

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