摘要翻译：
本文提供了一种新的方法来分析当观测特征被非线性建模时未观测到的非均质性。该模型建立在可变随机系数(VRC)基础上，可变随机系数由观测回归的非线性函数和可加可分的不可观测项决定。提出了一种新的基于加权筛分最小距离的VRC密度估计方法。筛基的主要例子是Hermite函数，它产生了一个数值稳定的估计过程。本文给出了超出一般RC模型的推论结果。我们给出了每种情况下的收敛速度，并建立了线性泛函的逐点极限理论，其中一个突出的例子是潜在结果的密度。此外，还提出了一个乘法器引导过程来构造一致的置信带。Monte Carlo研究检验了该估计量的有限样本性质，并表明即使与RC相关的回归子远离重尾，它也能很好地执行。最后，运用该方法分析了住房需求收入弹性的异质性。
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英文标题：
《Varying Random Coefficient Models》
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作者：
Christoph Breunig
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper provides a new methodology to analyze unobserved heterogeneity when observed characteristics are modeled nonlinearly. The proposed model builds on varying random coefficients (VRC) that are determined by nonlinear functions of observed regressors and additively separable unobservables. This paper proposes a novel estimator of the VRC density based on weighted sieve minimum distance. The main example of sieve bases are Hermite functions which yield a numerically stable estimation procedure. This paper shows inference results that go beyond what has been shown in ordinary RC models. We provide in each case rates of convergence and also establish pointwise limit theory of linear functionals, where a prominent example is the density of potential outcomes. In addition, a multiplier bootstrap procedure is proposed to construct uniform confidence bands. A Monte Carlo study examines finite sample properties of the estimator and shows that it performs well even when the regressors associated to RC are far from being heavy tailed. Finally, the methodology is applied to analyze heterogeneity in income elasticity of demand for housing.
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PDF链接：
https://arxiv.org/pdf/1804.03110