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Forecasting Time Series with Trading Day Variation
Author(s) -
Hillmer S. C.
Publication year - 1982
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980010406
Subject(s) - autoregressive integrated moving average , univariate , variation (astronomy) , series (stratigraphy) , econometrics , time series , seasonality , names of the days of the week , statistics , computer science , mathematics , multivariate statistics , paleontology , linguistics , physics , philosophy , astrophysics , biology
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.