arXiv 11 Oct 2022 · Econometrics
arXiv:2210.05115 · PDF · DOI · OpenAlex · Extracted main text
This study proposes a reversible jump Markov chain Monte Carlo method for estimating parameters of lognormal distribution mixtures for income. Using simulated data examples, we examined the proposed algorithm's performance and the accuracy of posterior distributions of the Gini coefficients. Results suggest that the parameters were estimated accurately. Therefore, the posterior distributions are close to the true distributions even when the different data generating process is accounted for. Moreover, promising results for Gini coefficients encouraged us to apply our method to real data from Japan. The empirical examples indicate two subgroups in Japan (2020) and the Gini coefficients' integrity.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gau SL, de Dieu\ Tapsoba J, Lee SM (2014) Bayesian approach for mixture models with grouped data | 0.874 | 7 | 2 | 100% |
| 2 | Richardson S, Green PJ (1997) On Bayesian analysis of mixtures with an unknown number of components (with discussion) | 0.874 | 6 | 2 | 100% |
| 3 | Lubrano M, Ndoye AAJ (2016) Income inequality decomposition using a finite mixture of log-normal distributions: A Bayesian approach | 0.737 | 3 | 2 | 100% |
| 4 | Wiper M, Rios Insua D, Ruggeri F (2001) Mixtures of gamma distributions with applications | 0.693 | 5 | 1 | 100% |
| 5 | McDonald JB (1984) Some generalized functions for the size distribution of income | 0.644 | 2 | 2 | 100% |
| 6 | Kakamu K, Nishino H (2019) Bayesian estimation of beta-type distribution parameters based on grouped data | 0.585 | 3 | 1 | 100% |
| 7 | Nishino H, Kakamu K (2011) Grouped data estimation and testing of Gini coefficients using lognormal distributions | 0.585 | 3 | 1 | 100% |
| 8 | Diebolt J, Robert CP (1994) Estimation of finite mixture distributions through Bayesian sampling | 0.511 | 2 | 1 | 100% |
| 9 | Eckernkemper T, Gribisch B (2021) Classical and Bayesian inference for income distributions using grouped data | 0.511 | 2 | 1 | 100% |
| 10 | Bordley RF, McDonald JB, Mantrala A (1997) Something new, something old: Parametric models for the size of distribution of income | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.