相关随机系数模型中出现的假设检验

Tests of Hypotheses Arising In the Correlated Random Coefficient Model.

作者信息

Heckman James J, Schmierer Daniel

机构信息

University of Chicago, University College Dublin, Cowles Foundation, Yale University, and the American Bar Foundation.

出版信息

Econ Model. 2010 Nov 1;27(6):1355-1367. doi: 10.1016/j.econmod.2010.07.019.

DOI:10.1016/j.econmod.2010.07.019

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3001628/

Abstract

This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance of the instrumental variable estimator for that model. We develop tests of the validity of the correlated random coefficient model against the null hypothesis of the uncorrelated random coefficient model.

摘要

本文研究了相关随机系数模型。它扩展了斯瓦米（1971年）的分析，斯瓦米开创了经济学中的不相关随机系数模型。我们推导了相关随机系数模型的性质，并得出了该模型工具变量估计量方差的一种新表示形式。我们针对不相关随机系数模型的原假设，开发了检验相关随机系数模型有效性的方法。

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Testing the Correlated Random Coefficient Model.检验相关随机系数模型。

J Econom. 2010 Oct 1;158(2):177-203. doi: 10.1016/j.jeconom.2010.01.005.

2

Efficient statistical inference procedures for partially nonlinear models and their applications.部分非线性模型的高效统计推断程序及其应用。

Biometrics. 2008 Sep;64(3):904-911. doi: 10.1111/j.1541-0420.2007.00937.x. Epub 2007 Nov 19.

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Local instrumental variables and latent variable models for identifying and bounding treatment effects.用于识别和界定治疗效果的局部工具变量和潜在变量模型。

Proc Natl Acad Sci U S A. 1999 Apr 13;96(8):4730-4. doi: 10.1073/pnas.96.8.4730.

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