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New ARIMA Models for Seasonal Time Series and Their Application to Seasonal Adjustment and Forecasting
New ARIMA Models for Seasonal Time Series and Their Application to Seasonal Adjustment and Forecasting
RRS2007-14
Abstract
Focusing on the widely-used Box-Jenkins “airline” model, we show how the class of seasonal ARIMA models with a seasonal moving average factor can be parsimoniously generalized to model time series with heteroskedastic seasonal frequency components. Our frequency-specific models decompose this factor by associating one moving average coefficient with a proper subset of the seasonal frequencies 1, 2, 3, 4, 5 and 6 cycles per year and a second coefficient with the complementary subset. A generalization of Akaike’s AIC is presented to determine these subsets. Properties of seasonal adjustment filters and adjustments obtained from the new models are examined as are forecasts.
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Page Last Revised - October 28, 2021
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