Abstract
This paper deals with modelling income distributions in the Czech Republic in 1992–2007. The net annual income per capita for Czech households is evaluated from data based on the microcensus and the EU-SILC 2005–2008. For all analysed years the distribution of incomes was estimated in the whole sample as well as in the subgroup of households, whose heads are physicists (or experts in related sciences), architects and engineers. Inthe paper the three-parametric lognormal distribution is used as a model. Unknownparameters are estimated with the use of four methods – those of maximum likelihood, quantiles, moments and L-moments.
Highlights
Statistical procedures commonly used for the description of the observed statistical sets lie in the use of their conventional moments, cumulants or quantiles
Experience shows that L-moments are less prone to estimation bias compared with conventional moments and in finite samples; they are closer to an asymptotic normal distribution
The first sample represents the part of the survey dealing with households in the Czech and Slovak Federal Republic, the country that split into the Czech Republic and the Slovak Republic in 1993
Summary
Statistical procedures commonly used for the description of the observed statistical sets lie in the use of their conventional moments, cumulants or quantiles. We fit the three-parametric lognormal distribution both to the whole sample and the subgroup of interest. All methods of estimation that are used in this paper, including the three-parametric lognormal distribution, are described in the statistical literature; see e.g. Bılkova (2008). The three-parameter lognormal distribution is discussed in detail, for example, in Bartosovaand Bına (2009) or in Bılkova (2008), a moment method of parameter estimation in Bartosova (2009) or Bılkova (2008), quantile method in Bılkova (2008) or Sipkovaand Sodomova (2009), maximum likelihood method in Bılkovaand Mala (2010). The lognormal distribution is used and the results of four different parameter estimation methods (those of moments, quantile, maximum likelihood and L-moments) are compared. The lognormal distribution is fitted into the sample and a subgroup of households including those whose heads are creative workers (“scientists and experts in physics and related sciences, architects and engineers”)
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