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Modeling and forecasting daily maximum hourly ozone concentrations using the RegAR model with skewed and heavy-tailed innovations

Authors
  • Sarnaglia, Alessandro José Queiroz1
  • Monroy, Nátaly Adriana Jiménez1
  • da Vitória, Arthur Gomes2
  • 1 Federal University of Espírito Santo, LECON - Statistics Department, Vitória, Brazil , Vitória (Brazil)
  • 2 Federal University of Espírito Santo, PIVIC Program, Vitória, Brazil , Vitória (Brazil)
Type
Published Article
Journal
Environmental and Ecological Statistics
Publisher
Springer US
Publication Date
Oct 12, 2018
Volume
25
Issue
4
Pages
443–469
Identifiers
DOI: 10.1007/s10651-018-0413-7
Source
Springer Nature
Keywords
License
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Abstract

This paper considers the modeling and forecasting of daily maximum hourly ozone concentrations in Laranjeiras, Serra, Brazil, through dynamic regression models. In order to take into account the natural skewness and heavy-tailness of the data, a linear regression model with autoregressive errors and innovations following a member of the family of scale mixture of skew-normal distributions was considered. Pollutants and meteorological variables were considered as predictors, along with some deterministic factors, namely week-days and seasons. The Oceanic Niño Index was also considered as a predictor. The estimated model was able to explain satisfactorily well the correlation structure of the ozone time series. An out-of-sample forecast study was also performed. The skew-normal and skew-t models displayed quite competitive point forecasts compared to the similar model with gaussian innovations. On the other hand, in terms of forecast intervals, the skewed models presented much better performance with more accurate prediction intervals. These findings were empirically corroborated by a forecast Monte Carlo experiment.

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