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Using mixture density functions for modelling of wage distributions. (English) Zbl 1345.62180
Summary: This article explores the possibility of modeling the wage distribution using a mixture of density functions. We deal with this issue for a long time and we build on our earlier work. Classical models use the probability distribution such as normal, lognormal, Pareto, etc., but the results are not very good in the last years. Changing the parameters of a probability density over time has led to a degradation of such models and it was necessary to choose a different probability distribution. We were using the idea of mixtures of distributions (instead of using one classical density) in previous articles. We tried using a mixture of probability distributions (normal, lognormal and a mixture of Johnson’s distribution densities) in our models. The achieved results were very good. We used data from Czech Statistical Office covering the wages of the last 18 years in Czech Republic.

##### MSC:
 62P20 Applications of statistics to economics
AS 99; SAS
Full Text:
##### References:
 [1] Bartošová J, Bína V (2007) Mixture models of household income distribution in the Czech Republic, Bratislava. In: 6th international conference APLIMAT 2007, part I. Bratislava: Slovak University of Technology, pp 307-316, ISBN 978-80-969562-4-1 [2] Bílková D (2012) Lognormal distribution and using L-moment method for estimating its parameters. Int J Math Models Method Appl Sci, 6, ISSN: 1998-0140 [3] George F (2011) Estimation of parameters of Johnson’s system of distributions. J Mod Appl Stat Methods 10(2), Article 911-1-2011, Florida International University [4] Hill, ID; Hill, R; Holder, RL, Fitting Johnson curves by moments, Appl Stat, 25, 180-189, (1976) [5] Hosking, JRM; Wallis, JR; Wood, EF, Estimation of the generalized extreme-value distribution by the method of probability-weighted moments, Technometrics, 27, 251-261, (1985) [6] http://www.jstor.org/stable/126846310.2307/1268463 [7] Johnson, NL, Systems of frequency curves generated by methods of translation, Biometrika, 36, 149-176, (1949) · Zbl 0033.07204 [8] Johnson, RJ, Estimating the size of a population, Teach Stat, 16, 50-52, (1994) [9] Kamziah, AK; Ahmad, MI; Jaffirin, L, Nonlinear regresion approach to estimating Johnson SB parameters for diameter data, Can J For Resour, 29, 310-314, (1999) [10] Marek L (2010) Analýza vývoje mezd v ČR v letech 1995-2008. In: Politická ekonomie, roč. 58, č. 2, s. 186-206. ISSN 0032-3233 · Zbl 0447.62020 [11] Marek L, Vrabec M (2010) Forecast of the income distribution in the Czech Republic in 2011. Ras Al Khaimah 29.11.2010-03.12.2010. In: ICABR 2010—VI. International conference on applied business research. Mendel University, Brno, s. 142. ISBN 978-80-7375-462-4 [12] Marek L, Vrabec M (2011) The using of normal mixture distribution for Wages models, Johor Bahru 28.11.2011-02.12.2011. In: ICCDA 2011—IC computer design and applications [CD-ROM]. Mendel University, Brno. s. 159. ISBN 978-80-7375-557-7 [13] Marek L, Vrabec M (2013) Wage distribution models, Rhodes Island. Recent advances in economics and business administration, pp. 184-188. In: EBA 2013—proceedings of the 2013 international conference on economics and business administration. ISBN 978-1-61804-198-2 · Zbl 0447.62020 [14] SAS Institute SYSTEM (2000) Base SAS(R) 9.2 procedures guide: statistical procedures, 3rd edn. SAS Institute Inc., Cary [15] Slifker, J; Shapiro, S, The Johnson system: selection and parameter estimation, Technometrics, 22, 239-247, (1980) · Zbl 0447.62020 [16] Wheeler, R, Quantile estimators of Johnson curve parameters, Biometrika, 67, 725-728, (1980)
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