Spatiotemporal Econometrics Models for Old Age Mortality in Europe
In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal pan...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/9/1061 |
| _version_ | 1797534780096512000 |
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| author | Patricia Carracedo Ana Debón |
| author_facet | Patricia Carracedo Ana Debón |
| author_sort | Patricia Carracedo |
| collection | DOAJ |
| description | In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them. |
| first_indexed | 2024-03-10T11:35:20Z |
| format | Article |
| id | doaj.art-20ec94e2ef8e4356aa388ce2891b4696 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T11:35:20Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-20ec94e2ef8e4356aa388ce2891b46962023-11-21T18:54:17ZengMDPI AGMathematics2227-73902021-05-0199106110.3390/math9091061Spatiotemporal Econometrics Models for Old Age Mortality in EuropePatricia Carracedo0Ana Debón1Área de Empresa, Universidad Internacional de Valencia, Pintor Sorolla, 21, 46002 Valencia, SpainCentro de Gestión de la Calidad y del Cambio, Universitat Politècnica de València, Camino de Vera, s/n, 46002 Valencia, SpainIn the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them.https://www.mdpi.com/2227-7390/9/9/1061panel dataspatiotemporal modelsEuropean mortality |
| spellingShingle | Patricia Carracedo Ana Debón Spatiotemporal Econometrics Models for Old Age Mortality in Europe Mathematics panel data spatiotemporal models European mortality |
| title | Spatiotemporal Econometrics Models for Old Age Mortality in Europe |
| title_full | Spatiotemporal Econometrics Models for Old Age Mortality in Europe |
| title_fullStr | Spatiotemporal Econometrics Models for Old Age Mortality in Europe |
| title_full_unstemmed | Spatiotemporal Econometrics Models for Old Age Mortality in Europe |
| title_short | Spatiotemporal Econometrics Models for Old Age Mortality in Europe |
| title_sort | spatiotemporal econometrics models for old age mortality in europe |
| topic | panel data spatiotemporal models European mortality |
| url | https://www.mdpi.com/2227-7390/9/9/1061 |
| work_keys_str_mv | AT patriciacarracedo spatiotemporaleconometricsmodelsforoldagemortalityineurope AT anadebon spatiotemporaleconometricsmodelsforoldagemortalityineurope |