Estimation and Applications of Quantile Regression for Binary Longitudinal Data

Mohammad Arshad Rahman () and Angela Vossmeyer

A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, 2019, vol. 40B, pp 157-191 from Emerald Group Publishing Limited

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.

Keywords: Bayesian inference; binary outcomes; female labor force participation; home ownership; limited dependent variables; panel data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Working Paper: Estimation and Applications of Quantile Regression for Binary Longitudinal Data (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532019000040b009

DOI: 10.1108/S0731-90532019000040B009

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