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Modeling Time Series With Calendar Variation
W. R. Bell and S. C. Hillmer
Journal of the American Statistical Association
Vol. 78, No. 383 (Sep., 1983), pp. 526-534
Stable URL: http://www.jstor.org/stable/2288114
Page Count: 9
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The modeling of time series data that include calendar variation is considered. Autocorrelation, trends, and seasonality are modeled by ARIMA models. Trading day variation and Easter holiday variation are modeled by regression-type models. The overall model is a sum of ARIMA and regression models. Methods of identification, estimation, inference, and diagnostic checking are discussed. The ideas are illustrated through actual examples.
Journal of the American Statistical Association © 1983 American Statistical Association