A Probabilistic Model for the Distribution of GDP per Capita in NUTS 3 Zones of Europe

  1. José Rodriguez-Avi 1
  1. 1 Universidad de Jaén
    info

    Universidad de Jaén

    Jaén, España

    ROR https://ror.org/0122p5f64

Revista:
Estudios de economía aplicada

ISSN: 1133-3197,  1697-5731

Año de publicación: 2022

Título del ejemplar: Sports Analytics within Sports Economics and Management

Volumen: 40

Número: 1

Tipo: Artículo

DOI: 10.25115/EEA.V40I1.5326 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: Estudios de economía aplicada

Resumen

A macroeconomic indicator of productivity and economic development, used to obtain information on the economic and social conditions of a country, is the GDP per capita, which is also used as an indicator of social welfare. By construction it can be used directly to compare areas of interest. It is an indicator of great variability to which it is difficult to assign a probabilistic model to describe its distribution. In fact, it usually appears as a strongly asymmetric and frequently multimodal variable, which directly indicates a strong non-normality. In this work we propose to deal with the problem of finding a probabilistic model for this variable through the estimation of a model of finite mixtures of normal distributions. As an application example, we present the model obtained through the finite mixture for GDP per capita data from the NUTS 3 zones in the nomenclature of the European Union, EU countries and neighbouring countries. Thus, the model is estimated, its validity is checked and the results obtained are analysed, both for the GDP per capita variable and as a function of the countries to which the studied areas belong.

Referencias bibliográficas

  • Benaglia T, Chauveau D, Hunter Dr & Young D, (2009). mixtools: An R Package for Analyzing Finite Mixture Models. Journal of Statistical Software, 32(6), 1-29. DOI 10.18637/jss.v032.i06
  • Hasselblad, V. (1966). Estimation of parameters for a mixture of normal distributions. Technometrics, 8(3):431-44.
  • Hasselblad, V. (1969). Estimation of finite mixtures of distributions from the exponen-tial family. Journal of the American Statistical Association, 64(328): 1459-71.
  • Huang T, Peng H & Zhang K. (2017) Model Selection for Gaussian Mixture Models. Statistica Sinica 27, 147-169. DOI 10.5705/ss.2014.105
  • Karlis D. (2005). EM algorithm for mixed Poisson and other discrete distributions. ASTIN Bulletin. 35(1):3-24.
  • Konte, M. (2018). Do remittances not promote growth? A finite mixture-of-regressions approach. Empir Econ 54:747–782. DOI 10.1007/s00181-016-1224-z
  • Liu, G.; Lee, C. C.; Liu, Y. (2020). Growth path heterogeneity across provincial economies in China: the role of geography versus institutions. Empirical Economics 59:503–546. DOI 10.1007/s00181-019-01639-y
  • Mclachlan, G.J.; Peel, D. (2000). Finite Mixture Models. Wiley Series in Probability and Statistics, New York.
  • Mclachlan G.J.; Krishnan T. (2008). The EM Algorithm and Extensions. 2nd ed. Hobo-ken, NJ: Jonh Wiley and Sons, Inc.
  • Mclachlan, G.J., Lee, S. X. and Rathnayake, S. I. (2019). Finite Mixture Models. Annual Review of Statistics and Its Application 6:355–78. DOI: 10.1146/annurev-statistics-031017-100325
  • Minamimuraa, K.; Yasuic, D. (2019). From physical to human capital accumulation: Effects of mortality changes. Review of Economic Dynamics, 34:103–120. DOI: 10.1016/j.red. 2019.03.005
  • Owen, A. L.; Videras, J; Davis, L. (2009). Do all countries follow the same growth pro-cess? J Econ Growth 14:265–286. DOI 10.1007/s10887-009-9046-x
  • Pani, A; Sahu, P. K.; Majumdar, B. B (2019). Expenditure-based segmentation of freight travel markets: Identifying the determinants of freight transport expenditure for developing marketing strategies. Research in Transportation Business & Management 33: 100437. DOI: 10.1016/j.rtbm.2020.100437
  • R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
  • Rao, C. (1948). The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society, Series B (Methodological), 10(2): 159-203.
Fuente de los datos: Dialnet