arXiv 18 May 2026 · Econometrics
arXiv:2605.18138 · PDF · DOI · OpenAlex · Extracted main text
Grouped income data contain only limited information about the evolution of income distributions over time. This paper develops a Bayesian state-space model for the generalized beta distribution of the second kind (GB2) to estimate dynamic income distributions using repeated grouped income data. By borrowing information across adjacent periods through the latent GB2 parameters, the proposed framework improves estimation precision relative to independent cross-sectional estimation. Building on the estimated latent-state dynamics, we further construct a model-based counterfactual framework that quantifies the contribution of demographic covariates while preserving the estimated evolution of the remaining model components. Using Japanese household income data from 1969--2007, we find that population aging and declining household size affect different parts of the income distribution through distinct channels, with population aging becoming an increasingly important driver of income inequality after around 2000. More generally, the proposed framework provides a unified Bayesian approach to dynamic distributional analysis and model-based counterfactual inference using repeated grouped income data.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Nishino, Haruhisa and Kakamu, Kazuhiko and Oga, Takashi (2012) Bayesian Estimation of Persistent Income Inequality Using the Lognormal Stochastic Volatility Model self | 1.000 | 9 | 3 | 100% |
| 2 | Genya Kobayashi and Yuta Yamauchi and Kazuhiko Kakamu and Yuki Kawak… (2022) Bayesian Approach to Lorenz Curve Using Time Series Grouped Data self | 1.000 | 5 | 4 | 100% |
| 3 | Daichi Hiraki and Yasuyuki Hamura and Kaoru Irie and Shonosuke Sugas… (2024) State-Space Modeling of Shape-constrained Functional Time Series | 0.928 | 4 | 3 | 100% |
| 4 | Kazuhiko Kakamu and Haruhisa Nishino (2019) Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data self | 0.843 | 3 | 3 | 100% |
| 5 | Haruhisa Nishino and Kazuhiko Kakamu (2015) A Random Walk Stochastic Volatility Model for Income Inequality self | 0.737 | 3 | 2 | 100% |
| 6 | Chernozhukov, Victor and Fernández-Val, Iván and Melly, Blaise (2013) Inference on Counterfactual Distributions | 0.644 | 2 | 2 | 100% |
| 7 | DiNardo, John and Fortin, Nicole M. and Lemieux, Thomas (1996) Labor Market Institutions and the Distribution of Wages, 1973–1992: A Semiparametric Approach | 0.644 | 2 | 2 | 100% |
| 8 | Fortin, Nicole and Lemieux, Thomas and Firpo, Sergio (2011) Decomposition Methods in Economics | 0.644 | 2 | 2 | 100% |
| 9 | Carter, C K and Kohn, R (1994) On Gibbs Sampling for State Space Models | 0.585 | 3 | 1 | 100% |
| 10 | Carlin, Bradley P and Polson, Nicholas G and Stoffer, David S (1992) A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 31 scored citations.