Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors

Chunli Huang, Xu Zhao, Weihu Cheng, Qingqing Ji, Qiao Duan and Yufei Han
Additional contact information
Chunli Huang: Faculty of Science, Beijing University of Technology, Beijing 100124, China
Xu Zhao: Faculty of Science, Beijing University of Technology, Beijing 100124, China
Weihu Cheng: Faculty of Science, Beijing University of Technology, Beijing 100124, China
Qingqing Ji: University of Chinese Academy of Sciences, Beijing 100049, China
Qiao Duan: Faculty of Humanities and Social Sciences, Beijing University of Technology, Beijing 100124, China
Yufei Han: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China

Mathematics, 2022, vol. 10, issue 9, 1-25

Abstract: Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM 2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM 2.5 concentration, achieving ideal results.

Keywords: generalized Pareto distribution; peaks over threshold; dynamic conditional autoregressive modeling; threshold selection; long short-term memory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/9/1433/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/9/1433/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:9:p:1433-:d:800987

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().