A1794
Title: Bayesian model averaging for income distribution
Authors: Haruhisa Nishino - Hiroshima University (Japan) [presenting]
Kazuhiko Kakamu - Nagoya City University (Japan)
Abstract: The aim is to estimate the generalized beta distribution of the second kind (GB2) with four parameters for the income distribution. It is known as helpful in analyzing income distribution. However, estimating the GB2 by the maximum likelihood estimation has challenges, such as difficulty choosing appropriate initial values, which can lead to unstable estimates. An alternative feasible Bayesian method is to estimate it using the Taylored randomized block Metropolis-Hastings (TaRBMH) algorithm. On the other hand, the GB2 distribution encompasses several three-parameter distributions as special cases, such as the Dagum distribution, the Singh-Maddala distribution, the Beta distribution of the second kind (B2), and the generalized Gamma distribution. Therefore, the Bayesian model is also explored averaging to estimate income distributions. The comparing result of the two Bayesian methods indicates that the latter required less computation time than the former. These methods are also evaluated on individual and group data from the comprehensive survey of living conditions and investigated the characteristics and dynamics of income distributions in Japan.
