Bayesian semiparametric quantile regression modeling for estimating earthquake fatality risk

Xuejun Jiang, Yunxian Li (), Aijun Yang and Ruowei Zhou
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Xuejun Jiang: Southern University of Science and Technology of China
Yunxian Li: Yunnan University of Finance and Economics
Aijun Yang: Nanjing Forestry University
Ruowei Zhou: Yunnan University of Finance and Economics

Empirical Economics, 2020, vol. 58, issue 5, No 3, 2085-2103

Abstract: Abstract This paper develops a Bayesian semiparametric quantile regression model for count data. The count responses are converted to continuous responses through the “jittered” method and a transform function. A Bayesian semiparametric quantile regression modeling approach is then developed. The error distribution in the quantile regression model is assumed to be a mixture of asymmetric Laplace distributions constructed with Dirichlet process. Historical death tolls of China caused by earthquakes from 1969 to 2006 are used for fitting, and a parametric model is employed for model comparison. The results of model comparison show that the proposed semiparametric quantile regression model outperforms the parametric model. The empirical analysis illustrates that the impact of earthquake magnitude on death tolls is significant. Moreover, the impact of the magnitude is more pronounced on higher percentiles of death tolls.

Keywords: Quantile regression; Bayesian semiparametric approach; Count data; Fatality risk (search for similar items in EconPapers)
JEL-codes: C11 C14 C51 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00181-018-1615-4

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