超越总量VAR：HANK模型的贝叶斯基准
Beyond Aggregate VARs: A Bayesian Benchmark for HANK Models
浏览论文内容
中文总结 AI 辅助
本文提出一个联合建模宏观总量与重复横截面边际分布的贝叶斯基准，用于估计HANK模型的分布效应，无需面板数据或独立密度估计。
中文摘要 AI 辅助
异质性主体新凯恩斯主义（HANK）模型刻画了整体横截面分布如何响应结构性冲击。传统代表性主体模型通常通过总量向量自回归（VAR）的脉冲响应进行校准。HANK模型缺乏可比拟的既定经验基准，因为它们不仅对总量作出预测，还对微观数据的分布作出预测。我们提出了一个贝叶斯基准，该基准联合建模宏观经济总量和来自重复横截面的若干边际分布，包括在不同调查中观测到的分布。我们的方法既可以在宏观经济总量上采用标准的结构VAR识别方法，也可以对微观数据施加识别限制。该模型提供了冲击分布效应的联合后验，无需家庭面板数据或独立的第一阶段密度估计。
英文摘要
Heterogeneous-agent New Keynesian (HANK) models characterize how entire cross-sectional distributions respond to structural shocks. Traditional representative-agent models are routinely disciplined by impulse responses from aggregate vector autoregressions (VARs). HANK models have no comparable established empirical benchmark because they make predictions not only about aggregates, but also about distributions of micro-level data. We propose a Bayesian benchmark that jointly models macroeconomic aggregates and several marginal distributions from repeated cross sections, including distributions observed in different surveys. Our approach can use both standard structural VAR identification approaches on macroeconomic aggregates and identification restrictions imposed on micro-level data. The model delivers a joint posterior of the distributional effects of shocks, without the need for household panel data or a separate first-stage density estimate.
