A Panel Data Estimator for the Distribution and Quantiles of Marginal Effects in Nonlinear Structural Models with an Application to the Demand for Junk Food
非线性结构模型中边际效应分布与分位数的面板估计：垃圾食品需求应用AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究面板数据中的边际效应分布与分位数，并以非线性结构模型为框架。
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非线性结构模型中边际效应分布与分位数的面板估计：垃圾食品需求应用
- 研究问题
- 在允许一般非线性、高维异质性及其持久成分与协变量相关的结构模型中，如何利用面板数据识别边际效应的分布？
- 方法与思路
- 作者利用两期面板构造导数型因变量，通过相邻子群的结果差异识别因果边际效应分布，并提出半参数样本估计量。
- 材料陈述的贡献
- 摘要称，对关注变量保持不变者建立构造性非参数识别，对变动者证明一般性不识别，所提估计量达到最小最大最优速率。
- 阅读关注点
- 核对不变者的定义、识别所需条件及其与总体效应的关系；关注分位数估计、最优速率证明和食品需求应用的解释范围。
- 阅读边界
- 仅据所给 OpenAlex 摘要，无法判断具体识别条件、速率表达式或应用估计值；家庭收入效应差异不能直接验证健康机制。
原文摘要
In this paper, we propose a framework to estimate the distribution of marginal effects in a general class of structural models that allow for very general nonlinearities, high dimensional heterogeneity, and unrestricted correlation between the persistent components of this heterogeneity and all covariates. The main idea is to form a derivative dependent variable using two periods of the panel and use differences in outcome variables of nearby subpopulations to obtain the distribution of causal marginal effects. We establish constructive nonparametric identification for the population of “stayers”, i.e., the subpopulation whose variable of interest stays constant between two time periods, and show generic non-identification for the “movers”. We propose natural semiparametric sample counterparts estimators, and establish that they achieve the optimal (minimax) rate. Moreover, we analyze their behavior through a Monte-Carlo study, and showcase the importance of allowing for nonlinearities and correlated heterogeneity through an application to demand for junk food. In this application, we establish profound differences in marginal income effects between poor and wealthy households which may at least partially explain health issues faced by the less privileged population.