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Logistic quantile regression in Stata

Author

Listed:
  • Nicola Orsini

    (Institute of Environmental Medicine, Karolinska Institutet)

  • Matteo Bottai

    (University of South Carolina
    Institute of Environmental Medicine, Karolinska Institutet)

Abstract

We present a set of Stata commands for the estimation, prediction, and graphical representation of logistic quantile regression described by Bottai, Cai, and McKeown (2010, Statistics in Medicine 29: 309–317). Logistic quantile regression models the quantiles of outcome variables that take on values within a bounded, known interval, such as proportions (or percentages) within 0 and 1, school grades between 0 and 100 points, and visual analog scales between 0 and 10 cm. We describe the syntax of the new commands and illustrate their use with data from a large cohort of Swedish men on lower urinary tract symptoms measured on the international prostate symptom score, a widely accepted score bounded between 0 and 35. Copyright 2011 by StataCorp LP.

Suggested Citation

  • Nicola Orsini & Matteo Bottai, 2011. "Logistic quantile regression in Stata," Stata Journal, StataCorp LP, vol. 11(3), pages 327-344, September.
    • Handle: RePEc:tsj:stataj:v:11:y:2011:i:3:p:327-344
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    References listed on IDEAS

    as
    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    2. Christopher F Baum, 2008. "Stata tip 63: Modeling proportions," Stata Journal, StataCorp LP, vol. 8(2), pages 299-303, June.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    5. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
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    3. Gustav Kjellsson & Dennis Petrie & Tom (T.G.M.) van Ourti, 2018. "Measuring income-related inequalities in risky health prospects," Tinbergen Institute Discussion Papers 18-007/V, Tinbergen Institute.
    4. L.A. Serkov & M.B. Petrov & K.B. Kozhov, 2021. "Modeling the Interaction of the Regions of Russia and the Republic of Belarus in the Sphere of the Processing Industry," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 217-240.
    5. Dong, Manh Cuong & Tian, Shaonan & Chen, Cathy W.S., 2018. "Predicting failure risk using financial ratios: Quantile hazard model approach," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 204-220.
    6. Louis Chauvel, 2014. "The Intensity and Shape of Inequality: The ABG Method of Distributional Analysis," LIS Working papers 609, LIS Cross-National Data Center in Luxembourg.

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