Otto, P. , Doğan, O. and Taşpınar, S. (2023) A dynamic spatiotemporal stochastic volatility model with an application to environmental risks. Econometrics and Statistics, (doi: 10.1016/j.ecosta.2023.11.002) (In Press)
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Abstract
A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical properties of an outcome variable within this model framework are examined, revealing the induction of spatial dependence in the outcome variable. Additionally, a Bayesian estimation procedure employing the Markov Chain Monte Carlo (MCMC) approach, complemented by a suitable data transformation, is presented. Simulation experiments are conducted to assess the performance of the proposed Bayesian estimator. Subsequently, the model is applied in the domain of environmental risk modeling, addressing the scarcity of empirical studies in this field. The significance of climate variation studies is emphasized, illustrated by an analysis of local air quality in Northern Italy during 2021, which underscores pronounced spatial and temporal clusters and increased uncertainties/risks during the winter season compared to the summer season.
Item Type: | Articles |
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Status: | In Press |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Otto, Dr Philipp |
Authors: | Otto, P., Doğan, O., and Taşpınar, S. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
Journal Name: | Econometrics and Statistics |
Publisher: | Elsevier |
ISSN: | 2452-3062 |
ISSN (Online): | 2452-3062 |
Published Online: | 08 November 2023 |
Copyright Holders: | Copyright © 2023 The Author(s) |
First Published: | First published in Econometrics and Statistics 2023 |
Publisher Policy: | Reproduced under a Creative Commons license |
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