Abstract
Predictive models are built for the mean ground concentration of ozone and nitrogen dioxide on data consisting of 5 years of daily averages. We find the smallest averaging time over which a model with a deterministic trend and an autoregressive error distribution passes a number of statistical tests. Such a model implies that the error variance conditioned on the past observations is constant. The fit is good for the series of weekly averages, but a model with heteroscedastic conditional variance has to be used for daily averages. The application of a generalized autoregressive heteroscedasticity model leads both to a satisfactory fit and a good predictive power for daily average data. © 1998 John Wiley & Sons, Ltd.
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