Comparing coefficients of nested nonlinear probability models
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Comparing coefficients of nested nonlinear probability models. / Kohler, Ulrich; Karlson, Kristian Bernt; Holm, Anders.
In: Stata Journal, Vol. 11, No. 3, 2011, p. 420-438.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Comparing coefficients of nested nonlinear probability models
AU - Kohler, Ulrich
AU - Karlson, Kristian Bernt
AU - Holm, Anders
PY - 2011
Y1 - 2011
N2 - In a series of recent articles, Karlson, Holm and Breen have developed amethod for comparing the estimated coeffcients of two nested nonlinear probability models. This article describes this method and the user-written program khb that implements the method. The KHB-method is a general decomposition method that is unaffected by the rescaling or attenuation bias that arise 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 GLM-family.
AB - In a series of recent articles, Karlson, Holm and Breen have developed amethod for comparing the estimated coeffcients of two nested nonlinear probability models. This article describes this method and the user-written program khb that implements the method. The KHB-method is a general decomposition method that is unaffected by the rescaling or attenuation bias that arise 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 GLM-family.
M3 - Journal article
VL - 11
SP - 420
EP - 438
JO - Stata Journal
JF - Stata Journal
SN - 1536-867X
IS - 3
ER -
ID: 44258801