Statistical Inference in an Implicit, Nonlinear, Simultaneous Equation Model in the Context of Maximum Likelihood Estimation

Econometrica ◽  
1981 ◽  
Vol 49 (2) ◽  
pp. 554
Author(s):  
A. Ronald Gallant ◽  
Alberto Holly
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Statistical Inference ◽  
Likelihood Estimation ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Simultaneous Equation Model

scholarly journals Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2098
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Jose J. López-Espín
Keyword(s):  
Maximum Likelihood ◽  
Covariance Structure ◽  
Likelihood Estimation ◽  
Optimization Method ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Tuning Parameters ◽  
Hybrid Metaheuristic ◽  
Error Terms ◽  
Simultaneous Equation Models

Problems in estimating simultaneous equation models when error terms are not intertemporally uncorrelated has motivated the introduction of a new multivariate model referred to as Multilevel Simultaneous Equation Model (MSEM). The maximum likelihood estimation of the parameters of an MSEM has been set forth. Because of the difficulties associated with the solution of the system of likelihood equations, the maximum likelihood estimator cannot be obtained through exhaustive search procedures. A hybrid metaheuristic that combines a genetic algorithm and an optimization method has been developed to overcome both technical and analytical limitations in the general case when the covariance structure is unknown. The behaviour of the hybrid metaheuristic has been discussed by varying different tuning parameters. A simulation study has been included to evaluate the adequacy of this estimator when error terms are not serially independent. Finally, the performance of this estimation approach has been compared with regard to other alternatives.

Sign in / Sign up

Export Citation Format

Share Document