Comparing coefficients between nested nonlinear probability models

In a series of recent articles, Karlson, Holm, and Breen have developed a method for comparing estimated coefficients of nested nonlinear probability models. 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. This presentation describes this method and the user-written program khb that implements the method.