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Estimation and Applications of Quantile Regression for Binary Longitudinal Data

In: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B

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  • Mohammad Arshad Rahman
  • Angela Vossmeyer

Abstract

This chapter 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.

Suggested Citation

  • Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532019000040b009
    DOI: 10.1108/S0731-90532019000040B009
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    Cited by:

    1. Arjun Gupta & Soudeh Mirghasemi & Mohammad Arshad Rahman, 2021. "Heterogeneity in food expenditure among US families: evidence from longitudinal quantile regression," Indian Economic Review, Springer, vol. 56(1), pages 25-48, June.
    2. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.
    3. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    4. Ivan Jeliazkov & Shubham Karnawat & Mohammad Arshad Rahman & Angela Vossmeyer, 2023. "Flexible Bayesian Quantile Analysis of Residential Rental Rates," Papers 2305.13687, arXiv.org, revised Sep 2023.
    5. Mohit Batham & Soudeh Mirghasemi & Mohammad Arshad Rahman & Manini Ojha, 2021. "Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States," Papers 2109.10122, arXiv.org, revised May 2023.

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