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.
The authors thank the anonymous referees, Ivan Jeliazkov, David Brownstone, John Geweke, K. L. Krishna, Antonio Galvao, Michael Guggisberg, and Editor Justin Tobias for their helpful comments. Discussions and suggestions from the participants at the Winter School, Delhi School of Economics (2017), and Advances in Econometrics Conference (2018) are appreciated. A special thanks to Dale Poirier for sharing the Reverend’s insights and teaching us the controversy.
Rahman, M. and Vossmeyer, A. (2019), "Estimation and Applications of Quantile Regression for Binary Longitudinal Data", Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B (Advances in Econometrics, Vol. 40B), Emerald Publishing Limited, pp. 157-191. https://doi.org/10.1108/S0731-90532019000040B009Download as .RIS
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