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Detection and Modeling of Trading Day Effects
Detection and Modeling of Trading Day Effects
Introduction
Monthly economic time series are often systematically influenced by effects specific to the seven days of the week and when or how often each of the seven days of the week occurs in the month. In flow series (e.g. monthly totals of daily sales), the presence of such an effect is revealed when the monthly values depend in a consistent way on which days of the week occur five times in the month. With retail grocery sales in the U.S., for example, the sales volume is smaller on Mondays, Tuesdays and Wednesdays than on days later in the week. Thus sales in March, say, will be relatively lower in a year in which March has an excess of early weekdays and higher when March has five Thursdays, Fridays and Saturdays. In addition, the average daily effect can give rise to a length-of-month effect and this effect is not completely absorbed into the seasonal component of the series because the length of February is not the same every year. The component of length-of-month effect that is not part of the seasonal component is called the leap year effect.
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Page Last Revised - October 8, 2021
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