论文标题
与随机效果相关的二元结果的贝叶斯小组分数回归：加拿大犯罪累犯的应用
Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada
论文作者
论文摘要
本文开发了一种贝叶斯的方法，用于估计面板的分位数回归，并在存在相关随机效应的情况下使用二元结果。我们使用不对称拉普拉斯（AL）误差分布构建工作可能性，并将其与合适的先验分布相结合以获得完整的关节后验分布。对于后推断，我们提出了两种马尔可夫链蒙特卡洛（MCMC）算法，但更喜欢利用阻止程序以在MCMC绘制中产生较低自相关的算法。我们还解释了如何使用MCMC绘制来计算边际效应，相对风险和优势比。在多个模拟研究中证明了我们首选算法的性能，并表明表现非常好。此外，我们使用行政惩教文件中的新数据实施了提出的框架来研究加拿大省魁北克的犯罪累犯。我们的结果表明，加拿大政府最近实施的“强硬犯罪”政策在很大程度上成功地降低了政治后时期的重复犯罪的可能性。此外，我们的结果还支持有关犯罪累犯的现有发现，并在各种分位数中提供新的见解。
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.
