Comparing coefficients of nested nonlinear probability models
Abstract. 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.
View all articles by these authors:
Ulrich Kohler, Kristian Bernt Karlson, Anders Holm
View all articles with these keywords:
khb, decomposition, path analysis, total effects, indirect effects, direct effects, logit, probit, primary effects, secondary effects, generalized linear model, KHB method
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