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Non‐linear statistical modelling of high frequency ground ozone data
Author(s) -
Fassò Alessandro,
Negri Ilia
Publication year - 2002
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.509
Subject(s) - heteroscedasticity , covariate , autoregressive model , econometrics , statistics , parametric statistics , environmental science , autoregressive integrated moving average , data set , computer science , mathematics , time series
The problem of describing hourly data of ground ozone is considered. The complexity of high frequency environmental data dynamics often requires models covering covariates, multiple frequency periodicities, long memory, non‐linearity and heteroscedasticity. For these reasons we introduce a parametric model which includes seasonal fractionally integrated components, self‐exciting threshold autoregressive components, covariates and autoregressive conditionally heteroscedastic errors with high tails. For the general model, we present estimation and identification techniques. To show the model descriptive capability and its use, we analyse a five year hourly ozone data set from an air traffic pollution station located in Bergamo, Italy. The role of meteo and precursor covariates, periodic components, long memory and non‐linearity is assessed. Copyright © 2002 John Wiley & Sons, Ltd.