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Spatiotemporal Econometrics Models for Old Age Mortality in Europe

Author

Listed:
  • Patricia Carracedo

    (Área de Empresa, Universidad Internacional de Valencia, Pintor Sorolla, 21, 46002 Valencia, Spain
    These authors contributed equally to this work.)

  • Ana Debón

    (Centro de Gestión de la Calidad y del Cambio, Universitat Politècnica de València, Camino de Vera, s/n, 46002 Valencia, Spain
    These authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Patricia Carracedo & Ana Debón, 2021. "Spatiotemporal Econometrics Models for Old Age Mortality in Europe," Mathematics, MDPI, vol. 9(9), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:1061-:d:551058
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    References listed on IDEAS

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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Debón, A. & Chaves, L. & Haberman, S. & Villa, F., 2017. "Characterization of between-group inequality of longevity in European Union countries," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 151-165.
    3. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    4. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    5. James W. Shaw & William C. Horrace & Ronald J. Vogel, 2005. "The Determinants of Life Expectancy: An Analysis of the OECD Health Data," Southern Economic Journal, Southern Economic Association, vol. 71(4), pages 768-783, April.
    6. O'Hare, Colin & Li, Youwei, 2014. "Is mortality spatial or social?," Economic Modelling, Elsevier, vol. 42(C), pages 198-207.
    7. David Cutler & Angus Deaton & Adriana Lleras-Muney, 2006. "The Determinants of Mortality," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 97-120, Summer.
    8. Hitiris, Theo & Posnett, John, 1992. "The determinants and effects of health expenditure in developed countries," Journal of Health Economics, Elsevier, vol. 11(2), pages 173-181, August.
    9. Neofytidou, Aliona & Fountas, Stilianos, 2020. "The impact of health on GDP: A panel data investigation," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    10. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    11. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    12. Denuit, Michel & Lang, Stefan, 2004. "Non-life rate-making with Bayesian GAMs," Insurance: Mathematics and Economics, Elsevier, vol. 35(3), pages 627-647, December.
    13. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    14. Robyn Swift, 2011. "The relationship between health and GDP in OECD countries in the very long run," Health Economics, John Wiley & Sons, Ltd., vol. 20(3), pages 306-322, March.
    15. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    16. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    17. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, December.
    18. James W. Shaw & William C. Horrace & Ronald J. Vogel, 2005. "The Determinants of Life Expectancy: An Analysis of the OECD Health Data," Southern Economic Journal, John Wiley & Sons, vol. 71(4), pages 768-783, April.
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