A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria
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 method...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/7/700 |
| _version_ | 1827696890294042624 |
|---|---|
| author | Belén Pérez-Sánchez Martín González Carmen Perea Jose J. López-Espín |
| author_facet | Belén Pérez-Sánchez Martín González Carmen Perea Jose J. López-Espín |
| author_sort | Belén Pérez-Sánchez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:57:39Z |
| format | Article |
| id | doaj.art-fdc0c2cc8083494e8fb2259743f94550 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T12:57:39Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-fdc0c2cc8083494e8fb2259743f945502023-11-21T11:50:51ZengMDPI AGMathematics2227-73902021-03-019770010.3390/math9070700A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter CriteriaBelén Pérez-Sánchez0Martín González1Carmen Perea2Jose J. López-Espín3Department of Statistics, Mathematics and Informatics, Miguel Hernández University, 03202 Elche, SpainCenter of Operations Research, Miguel Hernández University, 03202 Elche, SpainCenter of Operations Research, Miguel Hernández University, 03202 Elche, SpainCenter of Operations Research, Miguel Hernández University, 03202 Elche, SpainSimultaneous 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.https://www.mdpi.com/2227-7390/9/7/700simultaneous equations modelsbayesian method of momentsmarkov chain monte carloakaike information criteriaentropycomputational statistics |
| spellingShingle | Belén Pérez-Sánchez Martín González Carmen Perea Jose J. López-Espín A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria Mathematics simultaneous equations models bayesian method of moments markov chain monte carlo akaike information criteria entropy computational statistics |
| title | A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria |
| title_full | A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria |
| title_fullStr | A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria |
| title_full_unstemmed | A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria |
| title_short | A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria |
| title_sort | new computational method for estimating simultaneous equations models using entropy as a parameter criteria |
| topic | simultaneous equations models bayesian method of moments markov chain monte carlo akaike information criteria entropy computational statistics |
| url | https://www.mdpi.com/2227-7390/9/7/700 |
| work_keys_str_mv | AT belenperezsanchez anewcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT martingonzalez anewcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT carmenperea anewcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT josejlopezespin anewcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT belenperezsanchez newcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT martingonzalez newcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT carmenperea newcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria AT josejlopezespin newcomputationalmethodforestimatingsimultaneousequationsmodelsusingentropyasaparametercriteria |