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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

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    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, April.
    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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    Cited by:

    1. repec:eee:ecofin:v:44:y:2018:i:c:p:204-220 is not listed on IDEAS
    2. 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.
    3. 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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