Georges Bresson, Guy Lacroix, Mohammad Arshad Rahman
arXiv 25 Jan 2020 · Econometrics · publishedEmpirical Economics (2020) · 20 citations (OpenAlex)
arXiv:2001.09295 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rahman MA, Vossmeyer A (2019) Estimation and applications of quantile regression for binary longitudinal data | 1.000 | 14 | 3 | 100% |
| 2 | Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data | 0.843 | 3 | 3 | 100% |
| 3 | Luo Y, Lian H, Tian M (2012) Bayesian quantile regression for longitudinal data models | 0.811 | 4 | 2 | 100% |
| 4 | Mundlak Y (1978) On the pooling of time series and cross section data | 0.811 | 4 | 2 | 100% |
| 5 | Burda M, Harding M (2013) Panel probit with flexible correlated effects: Quantifying technology spillovers in the presence of latent heterogeneity | 0.737 | 3 | 2 | 100% |
| 6 | Chamberlain G (1984) Panel data | 0.737 | 3 | 2 | 100% |
| 7 | Chamberlain G (1982) Multivariate regression models for panel data | 0.737 | 3 | 2 | 100% |
| 8 | Geraci M, Bottai M (2007) Quantile regression for longitudinal data using the asymmetric Laplace distribution | 0.737 | 3 | 2 | 100% |
| 9 | Kozumi H, Kobayashi G (2011) Gibbs sampling methods for Bayesian quantile regression | 0.737 | 3 | 2 | 100% |
| 10 | Rahman MA (2016) Bayesian quantile regression for ordinal models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 84 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States | 0.405 | 1 | 1 |
| 2 | Flexible Bayesian Quantile Analysis of Residential Rental Rates | 0.405 | 1 | 1 |