TY  - CHAP
AB  - 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.
VL  - 40B
SN  - 978-1-83867-419-9, 978-1-83867-420-5/0731-9053
DO  - 10.1108/S0731-90532019000040B009
UR  - https://doi.org/10.1108/S0731-90532019000040B009
AU  - Rahman Mohammad Arshad
AU  - Vossmeyer Angela
PY  - 2019
Y1  - 2019/01/01
TI  - Estimation and Applications of Quantile Regression for Binary Longitudinal Data
T2  - Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B
T3  - Advances in Econometrics
PB  - Emerald Publishing Limited
SP  - 157
EP  - 191
Y2  - 2024/04/23
ER  -