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Open AccessArticle

Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach

by Fernanda Valente †,‡ and Márcio Laurini *,†,‡
FEARP-USP, Av. Bandeirantes, 3900-Vila Monte Alegre, Ribeirão Preto SP 14040-905, Brazil
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
We thank Guest Editor Claudio Morana and two anonymous referees for their valuable comments and criticisms.
Econometrics 2020, 8(2), 25; https://doi.org/10.3390/econometrics8020025
Received: 14 February 2020 / Revised: 5 June 2020 / Accepted: 8 June 2020 / Published: 11 June 2020
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposition of intensity function in components of trend, cycles, and spatial effects. In this model, spatial effects are also represented by a dynamic functional structure, which allows analyzing the possible changes in the spatio-temporal distribution of the occurrence of tornadoes due to possible changes in climate patterns. The model was estimated using Bayesian inference through the Integrated Nested Laplace Approximations. We use data from the Storm Prediction Center’s Severe Weather Database between 1954 and 2018, and the results provided evidence, from new perspectives, that trends in annual tornado occurrences in the United States have remained relatively constant, supporting previously reported findings. View Full-Text
Keywords: Spatial Point Process; Log Gaussian Cox Process; tornado occurrences Spatial Point Process; Log Gaussian Cox Process; tornado occurrences
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Valente, F.; Laurini, M. Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach. Econometrics 2020, 8, 25.

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