Estimation and inference for area-wise spatial income distributions from grouped data
Estimation and inference for area-wise spatial income distributions from grouped data
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DOI：
10.1016/j.csda.2019.106904
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
S. Sugasawa;Genya Kobayashi;Y. Kawakubo
中科院分区：
文献类型：
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作者：
S. Sugasawa;Genya Kobayashi;Y. Kawakubo
估计收入分配在衡量空间不平等和贫困方面发挥着重要作用。现有的收入分配文献主要集中于单独估计一个国家或一个地区的收入分布，而同时估计多个收入分布尽管具有实际重要性，但尚未得到讨论。为了克服这一困难，提出了考虑分组数据地理信息的同时估计和推断区域空间收入分布的有效方法。开发了一种有效的贝叶斯方法来估计和推断区域潜在参数，该方法给出了收入分布的区域汇总度量，例如平均收入和基尼指数，不仅适用于采样区域，而且还适用于由于潜在空间状态空间结构而没有任何样本的区域。使用日本各城市的分组收入数据演示了所提出的方法。模拟研究表明，所提出的方法相对于单独估计收入分布的粗略传统方法具有优越性。实现所提出方法的 R 代码可在 https://github.com/sshonosuke/SPID 上获取。
Estimating income distributions plays an important role in the measurement of inequality and poverty over space. The existing literature on income distributions predominantly focuses on estimating an income distribution for a country or a region separately and the simultaneous estimation of multiple income distributions has not been discussed in spite of its practical importance. To overcome the difficulty, effective methods are proposed for the simultaneous estimation and inference for area-wise spatial income distributions taking account of geographical information from grouped data. An efficient Bayesian approach to estimation and inference for area-wise latent parameters are developed, which gives area-wise summary measures of income distributions such as mean incomes and Gini indices, not only for sampled areas but also for areas without any samples thanks to the latent spatial state–space structure. The proposed method is demonstrated using the Japanese municipality-wise grouped income data. The simulation studies show the superiority of the proposed method to a crude conventional approach which estimates the income distributions separately. R code implementing the proposed methods is available at https://github.com/sshonosuke/SPID.