The Use Of Finite Mixtures Of Lognormal Distributions In The Modeling Of Incomes In The Czech Republic
Abstract
Finite mixtures of probability distributions are used for the modeling of probability distributions of incomes. These distributions are typically non-homogenous, heavy tailed and positively skewed. In the text a net annual incomes per capita of the Czech households in 2004 and 2008 are analysed. The finite mixtures of lognormal distributions are fitted into data from the survey Results of the Living Conditions Survey (a national module of the European Union Statistics on Income and Living Conditions (EU-SILC)) that has been held by the Czech Statistical Office since 2005. Firstly, the components with known group membership are formed according to the education of a head of a household (factor with 5 levels) and number of children (2 levels factor children yes/no and more detailed 5 levels factor) in the household. Secondly, data are divided into groups with unknown group membership in order to obtain the best possible fit. In this case 1 to 5 components in the mixture are used. All models fitted into data are compared with the use of Akaike criterion.
References
Bartošová, Jitka and Vladislav Bína. Modelling of Income Distribution of Czech Households in Years 1996 – 2005. Acta Oeconomica Pragensia. Vol. 17. Iss. 4. 3 − 18. 2009.
Bartošová, Jitka and Marie Forbelská. 2011. Differentiation and dynamics of household incomes in the Czech EU-SILC survey in the years 2005-2008. In: Kováčová, M. (ed.), 10th International Conference APLIMAT 2011, Bratislava, February 1–4, 2011, Proceedings. Slovak University of Technology, Bratislava, 2011, s. 1451-1460.
Bílková, Diana. Application of Lognormal Curves in Modeling of Wage Distributions. Journal of Applied Mathematics. Vol. 1. Iss. 2. 341 − 352. 2008.
CZSO, Czech Statistical Office. www.czso.cz.
CNB, Czech National bank. www.cnb.cz.
Flachaire, Emmanuel and Olivier Nunez. Estimation of the Income Distribution and Detection of Subpopulations: an Explanatory Model. Computational Statistics & Data Analysis. 2007.
Grün, Bettina and Friedrich Leisch. Flexmix version 2: Finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28(4):1-35, 2008.
Johnson, N. L., Narayanaswamy Balakrishnan and Samuel Kotz. Continuous Univariate Distributions. Vol. 1. New York: John Wiley & Sons, 1994.
McDonald, J.B. Some Generalized Functions for the Size Distribution of Income. Econometrica, Vol. 52, No. 3, 647-665, 1984.
Pavelka, Roman. Application of density mixture in the probability model construction of wage distributions, Applications of Mathematics and Statistics in Economy: AMSE 2009, Uherské hradiště, 2009, 341-350, 2009.
Titterington, D.M., A.F. Smith and U.E. Makov. Statistical analysis of finite mixture distributions, Wiley, 1985.
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