Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach
Fernanda Valente () and
Márcio Laurini ()
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Fernanda Valente: FEARP-USP, Av. Bandeirantes, 3900-Vila Monte Alegre, Ribeirão Preto SP 14040-905, Brazil
Econometrics, 2020, vol. 8, issue 2, 1-26
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.
Keywords: Spatial Point Process; Log Gaussian Cox Process; tornado occurrences (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:8:y:2020:i:2:p:25-:d:369918
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