Obtaining Simultaneous Equation Models through a Unified Shared-Memory Scheme of Metaheuristics

2011 ◽  
Author(s):  
Francisco Almeida ◽  
Domingo Gimenez ◽  
Jose J. Lopez-Espin
Keyword(s):  
Shared Memory ◽  
Simultaneous Equation ◽  
Memory Scheme ◽  
Simultaneous Equation Models

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

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.

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