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Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms

Author

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  • Rocío Hernández-Sanjaime

    (Center of Operations Research, Miguel Hernández University, 03202 Elche, Spain)

  • Martín González

    (Center of Operations Research, Miguel Hernández University, 03202 Elche, Spain)

  • Jose J. López-Espín

    (Center of Operations Research, Miguel Hernández University, 03202 Elche, Spain)

Abstract

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.

Suggested Citation

  • Rocío Hernández-Sanjaime & Martín González & Jose J. López-Espín, 2020. "Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms," Mathematics, MDPI, vol. 8(12), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2098-:d:449939
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    References listed on IDEAS

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    4. Fiona Steele, 2003. "Selection effects of source of contraceptive supply in an analysis of discontinuation of contraception: multilevel modelling when random effects are correlated with an explanatory variable," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(3), pages 407-423, October.
    5. Kamel Jedidi & Harsharanjeet S. Jagpal & Wayne S. DeSarbo, 1997. "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, INFORMS, vol. 16(1), pages 39-59.
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