scholarly journals Random coefficients on endogenous variables in simultaneous equations models

2014 ◽  
Author(s):  
Matthew Masten
Keyword(s):  
Random Coefficients ◽  
Simultaneous Equations ◽  
Simultaneous Equations Models ◽  
Endogenous Variables

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.

scholarly journals A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 700
Author(s):  
Belén Pérez-Sánchez ◽  
Martín González ◽  
Carmen Perea ◽  
Jose J. López-Espín
Keyword(s):  
Experimental Study ◽  
Computational Study ◽  
Estimation Method ◽  
Information Criteria ◽  
Simultaneous Equations ◽  
Estimation Methods ◽  
Entropy Measure ◽  
Economic Science ◽  
Akaike Information Criteria ◽  
Simultaneous Equations Models

Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

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