The three most important Chinese holidays, Chinese New Year, the
Dragon-boat Festival, and Mid-Autumn Holiday have dates determined
by a lunar calendar and move between two solar months. Consumption,
production, and other economic behavior in countries with large
Chinese population including Taiwan are strongly affected by these
holidays. For example, production accelerates be-fore lunar new
year, almost completely stops during the holidays and gradually
rises to an average level after the holidays. This moving holiday
often creates difficulty for empirical modeling using monthly data
and this paper employs an approach that uses regressors for each
holiday to distin-guish effects before, during and after holiday.
Assuming that the holiday effect is the same for each day of the
interval over which the regressor is nonzero in a given year, the
value of the regressor in a given month is the proportion of this
interval that falls in the month. Bell and Hillmer (1983) proposed
such a regressor for Easter which is now extensively used in the
U.S. and Europe. We apply the Bell and Hillmer’s method to
analyze ten important series in Taiwan, which might be af-fected by
moving holidays. AICC and out-of-sample forecast performance were
used for selecting number of holiday regressors and their interval
lengths. The results are further checked by various diagnostic
checking statistics including outlier detection and sliding spans
analysis. The empirical results support this approach. Adding
holiday regressors can effectively control the impact of mov-ing
holidays and improves the seasonal decomposition. AICC and
accumulated forecast error are useful in regressor selection. We
find that unemployment rates in Taiwan have holiday effects and
seasonal factors cannot be consistently estimated unless the
holiday factor is included. Further-more, as the unemployment is
rising, the magnitude of holiday and seasonal factor are
decreasing. Finally, we find that holiday factors are generally
smaller than seasonal factors but should not be ignored.
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