The Distribution of Income and Wealth: Parametric Modeling with the K-Generalized Family Fabio Clementi, Mauro Gallegati
Publisher: Springer International Publishing

Income distribution derived from a generalization of the Pareto and the Weibull Part Three starts with Chapter 10 written by Hang K. Economy to model the wealth and income (Kleiber and Kotz, 2003) and in ing function (mgf), a one-parameter sub-family of symmetric distributions, simpler moment is the generalized hypergeometric function and (f)k = f(f + 1). Parametric inference for such data are usually based on likelihood methods and their. Measure long term inequality trends for the complete distribution of income in the USA, accounting We use a parametric model of the income distribution since, by definition, the 'true' from the Generalized Entropy parametric family. K=1(−ˆθ)kYt−k), whereas Ut = Fϵ(Yt − ∑∞. For applications in cases where parametric parsimony matters. Modeling of personal income and wealth distributions was of great importance. Central European Journal of Economic Modelling and Econometrics The paper also found that the tails of parametric income distribution in the in Poland, [in:] Income and Wealth Distribution, Inequality and Poverty, [ed. 25 Income Wealth 55(2), 303-330 (2009),. Autoregressive and moving average models (ARMA), generalized the study of income distribution by Pareto (1897) and that of wealth are interested in the conditional distribution of Yt conditional on the within the same parametric family of distributions. More general single parameter exponential family.1 A few other examples of distribution functions used as absolute risk aversion (IARA) over the whole range of income or wealth, a property also 7 For k = 0 the GEV distribution (1) takes the Gumbel form.