Forecasting Time Series with Trading Day Variation

Some levels of economic activity change over the days of the week. Also, the composition of the calendar changes over the years so that a particular month contains a different configuration of days of the week each year. The effects of the changing composition of the calendar upon a monthly time ser...

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Bibliographic Details
Published in:Journal of forecasting 1982-10, Vol.1 (4), p.385-395
Main Author: Hillmer, S. C.
Format: Article
Language:English
Subjects:
ARIMA models
Calendar variation
Forecast/Forecasts/Forecasting
Series
Time
Time series
Trading day variation
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Summary:Some levels of economic activity change over the days of the week. Also, the composition of the calendar changes over the years so that a particular month contains a different configuration of days of the week each year. The effects of the changing composition of the calendar upon a monthly time series is called trading day variation. This paper discusses one way to model trading day variation in monthly time series and how this model can be used to obtain improved forecasts over univariate ARIMA models. The ideas are illustrated on an actual data set.
ISSN:0277-6693
1099-131X
DOI:10.1002/for.3980010406