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Estimation and Applications of Quantile Regression for Binary Longitudinal Data

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  • Mohammad Arshad Rahman
  • Angela Vossmeyer

Abstract

This paper develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

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  • Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Papers 1909.05560, arXiv.org.
    • Handle: RePEc:arx:papers:1909.05560
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    References listed on IDEAS

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    1. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    2. Ivan Jeliazkov & Angela Vossmeyer, 2018. "The impact of estimation uncertainty on covariate effects in nonlinear models," Statistical Papers, Springer, vol. 59(3), pages 1031-1042, September.
    3. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.
    4. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    5. Christian A. L. Hilber & Tracy M. Turner, 2014. "The Mortgage Interest Deduction and its Impact on Homeownership Decisions," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 618-637, October.
    6. Geoffrey Carliner, 1974. "Determinants of Home Ownership," Land Economics, University of Wisconsin Press, vol. 50(2), pages 109-119.
    7. Martin Burda & Matthew Harding, 2013. "Panel Probit With Flexible Correlated Effects: Quantifying Technology Spillovers In The Presence Of Latent Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 956-981, September.
    8. Turner, Tracy M. & Luea, Heather, 2009. "Homeownership, wealth accumulation and income status," Journal of Housing Economics, Elsevier, vol. 18(2), pages 104-114, June.
    9. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2018. "Bayesian quantile regression for ordinal longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 815-828, April.
    10. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108423380, December.
    11. James J. Heckman & Thomas MaCurdy, 1982. "Corrigendum on A Life Cycle Model of Female Labour Supply," Review of Economic Studies, Oxford University Press, vol. 49(4), pages 659-660.
    12. Tracy M. Turner & Marc T. Smith, 2009. "Exits From Homeownership: The Effects Of Race, Ethnicity, And Income," Journal of Regional Science, Wiley Blackwell, vol. 49(1), pages 1-32, February.
    13. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
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    Cited by:

    1. Arjun Gupta & Soudeh Mirghasemi & Mohammad Arshad Rahman, 2020. "Heterogeneity in Food Expenditure amongst US families: Evidence from Longitudinal Quantile Regression," Papers 2010.02614, arXiv.org.

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