Comparing coefficients of nested nonlinear probability models

Research output: Contribution to journalJournal articlepeer-review

Standard

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 journalJournal articlepeer-review

Harvard

Kohler, U, Karlson, KB & Holm, A 2011, 'Comparing coefficients of nested nonlinear probability models', Stata Journal, vol. 11, no. 3, pp. 420-438.

APA

Kohler, U., Karlson, K. B., & Holm, A. (2011). Comparing coefficients of nested nonlinear probability models. Stata Journal, 11(3), 420-438.

Vancouver

Kohler U, Karlson KB, Holm A. Comparing coefficients of nested nonlinear probability models. Stata Journal. 2011;11(3):420-438.

Author

Kohler, Ulrich ; Karlson, Kristian Bernt ; Holm, Anders. / Comparing coefficients of nested nonlinear probability models. In: Stata Journal. 2011 ; Vol. 11, No. 3. pp. 420-438.

Bibtex

@article{e578e83e08d14f679549106c57000b5d,
title = "Comparing coefficients of nested nonlinear probability models",
abstract = "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.",
author = "Ulrich Kohler and Karlson, {Kristian Bernt} and Anders Holm",
year = "2011",
language = "English",
volume = "11",
pages = "420--438",
journal = "Stata Journal",
issn = "1536-867X",
publisher = "Stata Press",
number = "3",

}

RIS

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