Nonlinear statistical modelling of high frequency ground ozone data

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.