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Confidence intervals for predicted outcomes in regression models for categorical outcomes

Author

Listed:
  • Jun Xu

    (Indiana University)

  • J. Scott Long

    (Indiana University)

Abstract

We discuss methods for computing confidence intervals for predictions and discrete changes in predictions for regression models for categorical outcomes. The methods include endpoint transformation, the delta method, and bootstrap- ping. We also describe an update to prvalue and prgen from the SPost package, which adds the ability to compute confidence intervals. The article provides several examples that illustrate the application of these methods. Copyright 2005 by StataCorp LP.

Suggested Citation

  • Jun Xu & J. Scott Long, 2005. "Confidence intervals for predicted outcomes in regression models for categorical outcomes," Stata Journal, StataCorp LP, vol. 5(4), pages 537-559, December.
    • Handle: RePEc:tsj:stataj:v:5:y:2005:i:4:p:537-559
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    References listed on IDEAS

    as
    1. Weihua Guan, 2003. "From the help desk: Bootstrapped standard errors," Stata Journal, StataCorp LP, vol. 3(1), pages 71-80, March.
    2. Brian P. Poi, 2004. "From the help desk: Some bootstrapping techniques," Stata Journal, StataCorp LP, vol. 4(3), pages 312-328, September.
    3. Tim Liao, 2000. "Estimated Precision for Predictions from Generalized Linear Models in Sociological Research," Quality & Quantity: International Journal of Methodology, Springer, vol. 34(2), pages 137-152, May.
    4. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    Full references (including those not matched with items on IDEAS)

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