Pareto extrapolation: An analytical framework for studying tail inequality

• 成果类型: Article
• 署名作者: Gouin-Bonenfant, Emilien; Toda, Alexis Akira
• 署名单位: Columbia University; University of California System; University of California San Diego
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1817
• 发表日期: 2023
• 页码: 201-233
• 关键词: Bewley-Huggett-Aiyagari model; Pareto exponent; power law; solution accuracy; C63; D31; D58; E21

摘要

We develop an analytical framework designed to solve and analyze heterogeneous-agent models that endogenously generate fat-tailed wealth distributions. We exploit the asymptotic linearity of policy functions and the analytical characterization of the Pareto exponent to augment the conventional solution algorithm with a theory of the tail. Our framework allows for a precise understanding of the very top of the wealth distribution (e.g., analytical expressions for top wealth shares, type distribution in the tail, and transition probabilities in and out of the tail) in addition to delivering improved accuracy and speed.