Ivan Jeliazkov, Shubham Karnawat, Mohammad Arshad Rahman, Angela Vossmeyer
arXiv 23 May 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2305.13687 · PDF · DOI · OpenAlex · Extracted main text
This article develops a random effects quantile regression model for panel data that allows for increased distributional flexibility, multivariate heterogeneity, and time-invariant covariates in situations where mean regression may be unsuitable. Our approach is Bayesian and builds upon the generalized asymmetric Laplace distribution to decouple the modeling of skewness from the quantile parameter. We derive an efficient simulation-based estimation algorithm, demonstrate its properties and performance in targeted simulation studies, and employ it in the computation of marginal likelihoods to enable formal Bayesian model comparisons. The methodology is applied in a study of U.S. residential rental rates following the Global Financial Crisis. Our empirical results provide interesting insights on the interaction between rents and economic, demographic and policy variables, weigh in on key modeling features, and overwhelmingly support the additional flexibility at nearly all quantiles and across several sub-samples. The practical differences that arise as a result of allowing for flexible modeling can be nontrivial, especially for quantiles away from the median.
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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, M. A. and Karnawat, S (2019) Flexible Bayesian Quantile Regression in Ordinal Models self | 0.874 | 5 | 2 | 100% |
| 2 | Chib, S. and Jeliazkov, I (2001) Marginal Likelihood from the Metropolis-Hastings Output self | 0.811 | 4 | 2 | 100% |
| 3 | Luo, Y., Lian, H., and Tian, M (2012) Bayesian Quantile Regression for Longitudinal Data Models | 0.811 | 4 | 2 | 100% |
| 4 | Chib, S (1995) Marginal Likelihood from the Gibbs Output | 0.737 | 3 | 2 | 100% |
| 5 | Kozumi, H. and Kobayashi, G (2011) Gibbs Sampling Methods for Bayesian Quantile Regression | 0.737 | 3 | 2 | 100% |
| 6 | Yan, Y. and Kottas, A (2017) A New Family of Error Distributions for Bayesian Quantile Regression | 0.737 | 3 | 2 | 100% |
| 7 | Chib, S. and Jeliazkov, I (2006) Inference in Semiparametric Dynamic Models for Binary Longitudinal Data self | 0.644 | 2 | 2 | 100% |
| 8 | Greenberg, E (2012) Introduction to Bayesian Econometrics | 0.585 | 3 | 1 | 100% |
| 9 | Kobayashi, G. and Kozumi, H (2012) Bayesian Analysis of Quantile Regression for Censored Dynamic Panel Data Model | 0.511 | 2 | 1 | 100% |
| 10 | Maheshwari, P. and Rahman, M. A (2023) bqror: An R package for Bayesian Quantile Regression in Ordinal Models self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 38 scored citations.