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基于汇总表数据的免调参非参数密度估计AI 解读

Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation…

帕累托外推：研究尾部不平等的分析框架AI 解读

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…

基于顶端收入份额的帕累托指数有效最小距离估计AI 解读

Summary We propose an efficient estimation method for the income Pareto exponent when only certain top income shares are observable. Our estimator is based on the asymptotic theory of weighted sums of order statistics and the efficient minimum distance estimator. Simulations show that our estimator has excellent finite‐sample properties. We apply our estimation method to US top income share data and find that the Pareto exponent has been ranging between 1.4 and 1.8 since…

消费中的双侧幂律及其对欧拉方程检验的影响AI 解读

We provide evidence suggesting that the cross-sectional distributions of US consumption and its growth rate obey the power law in both the upper and lower tails, with exponents approximately equal to four. Consequently, high-order moments are unlikely to exist, and the generalized method of moments estimation of Euler equations that employs cross-sectional moments may be inconsistent. Through bootstrap studies, we find that the power law appears to generate spurious…