数学
非线性系统
统计
广义线性模型
应用数学
嵌套集模型
变量(数学)
计量经济学
计算机科学
数据挖掘
数学分析
关系数据库
量子力学
物理
作者
Ulrich Köhler,Kristian Bernt Karlson,Anders Holm
出处
期刊:Stata Journal
[SAGE]
日期:2011-10-01
卷期号:11 (3): 420-438
被引量:649
标识
DOI:10.1177/1536867x1101100306
摘要
In a series of recent articles, Karlson, Holm, and Breen (Breen, Karlson, and Holm, 2011, http://papers.ssrn.com/sol3/papers.cfm?abstractid=1730065 ; Karlson and Holm, 2011, Research in Stratification and Social Mobility 29: 221– 237; Karlson, Holm, and Breen, 2010, http://www.yale.edu/ciqle/Breen Scaling %20effects.pdf) have developed a method for comparing the estimated coefficients of two nested nonlinear probability models. In this article, we describe this method and the user-written program khb, which implements the method. The KHB method is a general decomposition method that is unaffected by the rescaling or attenuation bias that arises in cross-model comparisons in nonlinear models. It recovers the degree to which a control variable, Z, mediates or explains the relationship between X and a latent outcome variable, Y * , underlying the nonlinear probability model. It also decomposes effects of both discrete and continuous variables, applies to average partial effects, and provides analytically derived statistical tests. The method can be extended to other models in the generalized linear model family.
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