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Comparing linear probability model coefficients across groups


  • Anders Holm
  • Mette Ejrnæs
  • Kristian Karlson


This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models (LPM) between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using LPM coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate these limitations, and we suggest a restricted approach to using LPM coefficients in-group comparisons. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Anders Holm & Mette Ejrnæs & Kristian Karlson, 2015. "Comparing linear probability model coefficients across groups," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 1823-1834, September.
  • Handle: RePEc:spr:qualqt:v:49:y:2015:i:5:p:1823-1834
    DOI: 10.1007/s11135-014-0057-0

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    References listed on IDEAS

    1. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 45(3), pages 809-829, August.
    2. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 809-829, August.
    3. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    4. Greene, William, 2010. "Testing hypotheses about interaction terms in nonlinear models," Economics Letters, Elsevier, vol. 107(2), pages 291-296, May.
    5. Edward C. Norton & Hua Wang & Chunrong Ai, 2004. "Computing interaction effects and standard errors in logit and probit models," Stata Journal, StataCorp LP, vol. 4(2), pages 154-167, June.
    6. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    7. Richard Breen & Anders Holm & Kristian Bernt Karlson, 2014. "Correlations and Nonlinear Probability Models," Sociological Methods & Research, , vol. 43(4), pages 571-605, November.
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