Semiparametric estimation of consumer demand systems in real expenditure
以实际支出为基础的消费者需求系统半参数估计AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要从成本函数构造消费者需求系统,讨论可积性、理性限制和消费者剩余计算。
以实际支出为基础的消费者需求系统半参数估计AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Abstract Microdata concerning consumer demand typically show considerable variation in real expenditures, but very little variation in prices. We propose a semiparametric strategy for the consumer demand problem in which expenditure share equations are estimated nonparametrically in the real expenditure direction and estimated parametrically (with fixed or varying coefficients) in price directions. In our model, Engel curves are unrestricted: demands may have any rank. Because the demand model is derived from a cost function, it may be restricted to satisfy integrability and used for consumer surplus calculations. Since real expenditure is unobserved, but rather estimated under the model, we face a semiparametric model with a nonparametrically generated regressor. We show efficient convergence rates for parametric and nonparametric components. We illustrate the feasibility of our proposed strategy using Canadian expenditure and price data: Engel curves display curvature which cannot be encompassed by standard parametric models. We also find that the rationality restriction of Slutsky symmetry is rejected in the fixed‐coefficients model, but not in the varying‐coefficients model. Copyright © 2009 John Wiley & Sons, Ltd.
BLP 模型中的条件矩估计与推断
The random-coefficient demand model of Berry, Levinsohn, and Pakes (1995) is commonly estimated by the generalized method of moments (GMM), using an unconditional moment restriction with a fixed set of instruments. Identification of the model, however, rests on a conditional moment restriction. The two are not equivalent: the unconditional restriction may admit additional parameter values. We construct a counterexample in which the model is identified by the conditional…
战略网络形成模型中的单网络有限样本推断
We develop a finite-sample valid inference procedure for strategic network formation models with endogenous network statistics, using only a single observed network. We impose no restrictions on network density, equilibrium selection, or the dependence structure induced by strategic interaction. Using a "bounding-by-c" technique, we obtain realization-wise sandwich inequalities whose middle term depends only on exogenous covariates and i.i.d. pairwise shocks. These…
一般处理模型的高阶去偏估计量
We have witnessed tremendous progress in developing the foundation for econometrics and causal inference in the past decades. The most popular paradigm is the classical (first-order) semiparametric theory, in which a key building block is the (first-order) influence function. However, it is well known that estimators based on influence functions can be sub-optimal in various settings. To address this issue, higher-order influence functions (HOIF) are developed. However, most…
产品内生进入与退出下需求模型的识别和估计
Firms introduce products in markets where they anticipate stronger demand, using information unobserved by researchers. This creates endogenous selection in demand estimation. In differentiated-product oligopolies, multidimensional demand unobservables and strategic entry can render the ordinary propensity score insufficient for selection correction. Existing approaches either restrict firms' information at entry or jointly estimate demand, pricing, and entry under strong…
一般处理模型中的高阶去偏估计器
It is now well known that estimators based on influence functions can be sub-optimal in terms of convergence rates in various settings. To address this issue, higher-order influence functions (HOIF) are developed, generalizing the classical semiparametric theory. However, most existing results in this regard focus on treatment effect parameters defined in explicit forms, such as average treatment effects (ATE). In applications, economists are often confronted with tasks of…
基于机器学习的去偏 U 统计量
We propose a method to debias estimators based on U-statistics with machine-learning (ML) first steps. Standard plug-in estimators often suffer from regularization and model-selection biases, leading to invalid inference. We characterize orthogonal adjustment terms for two-step U-statistics, construct Debiased Machine Learning (DML) U-estimators, develop a cross-fitting algorithm, and establish a general asymptotic theory for inference with ML first steps. We illustrate the…