Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/215324 
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 12928
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain the complete joint posterior distribution. For posterior inference, we propose two Markov chain Monte Carlo (MCMC) algorithms but prefer the algorithm that exploits the blocking procedure to produce lower autocorrelation in the MCMC draws. We also explain how to use the MCMC draws to calculate the marginal effects, relative risk and odds ratio. The performance of our preferred algorithm is demonstrated in multiple simulation studies and shown to perform extremely well. Furthermore, we implement the proposed framework to study crime recidivism in Quebec, a Canadian Province, using a novel data from the administrative correctional files. Our results suggest that the recently implemented "tough-on-crime" policy of the Canadian government has been largely successful in reducing the probability of repeat offenses in the post-policy period. Besides, our results support existing findings on crime recidivism and offer new insights at various quantiles.
Subjects: 
Bayesian inference
correlated random effects
crime
panel data
quantile regression
recidivism
JEL: 
C11
C31
C33
C35
K14
K42
Document Type: 
Working Paper

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