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Generalizations of the Box-Jenkins Airline Model with Frequency-Specific Seasonal Coefficients and a Generalizaton of Akaike’s MAIC
Generalizations of the Box-Jenkins Airline Model with Frequency-Specific Seasonal Coefficients and a Generalizaton of Akaike’s MAIC
Abstract
The Box-Jenkins “airline” model is the most widely used ARIMA model for seasonal time series. Findley, Martin and Wills (2002) examined a generalization of the airline model with a more restricted seasonal moving average factor that models only seasonal effects and with a second-order nonseasonal moving average factor. Here, we generalize the seasonal factor further by associating a subset of the frequencies 1, 2, 3, 4, 5 and 6 cycles per year (in the case of monthly data) with one coefficient and the complementary subset with a second coefficient. A generalization of Akaike’s Minimum AIC criterion is presented for choosing among subsets of a given size or, more generally, among generalizations of the airline model having the same number of coefficients. Properties of modelbased seasonal adjustment filters obtained from the new models are considered as well as forecasting performance relative to the airline model.
Others in Series
Working Paper
Toward Variances for X-11 Seasonal Adjustments
We develop an approach to estimating variances for X-11 seasonal adjustments that recognizes the effects of sampling error and errors from forecast extension.
Working Paper
A Non-Gaussian Airline Model for Seasonal Adjustment
The Airline model, introduced by Box and Jenkins in their seminal book, is routinely used to model economic time series.
Working Paper
Issues in Identifying Easter Effects in Economic Time Series
This paper shows that analysis of X-11 extreme values and regARIMA outliers can contribute to better Easter effect decisions.
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Page Last Revised - October 8, 2021
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