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Comparing coefficients of nested nonlinear probability models

Author

Listed:
  • Ulrich Kohler

    () (Wissenschaftszentrum Berlin)

  • Kristian Bernt Karlson

    () (Aarhus University)

  • Anders Holm

    () (Aarhus University)

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 effects.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 nonlin- ear 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.

Suggested Citation

  • Ulrich Kohler & Kristian Bernt Karlson & Anders Holm, 2011. "Comparing coefficients of nested nonlinear probability models," Stata Journal, StataCorp LP, vol. 11(3), pages 420-438, September.
  • Handle: RePEc:tsj:stataj:v:11:y:2011:i:3:p:420-438
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    References listed on IDEAS

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    1. Bruno S. Frey & Alois Stutzer, 2002. "What Can Economists Learn from Happiness Research?," Journal of Economic Literature, American Economic Association, vol. 40(2), pages 402-435, June.
    2. Jann, Ben, 2007. "Making regression tables simplified," Stata Journal, StataCorp LP, vol. 7(2), pages 1-18.
    3. Maarten L. Buis, 2010. "Direct and indirect effects in a logit model," Stata Journal, StataCorp LP, vol. 10(1), pages 11-29, March.
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