@ARTICLE{26543120_109002177_2013, 
	author = {Vladimir Bessonov and Anna Petronevich}, 
	keywords = {, seasonal adjustment, crisis, monitoring, short-term trends, economic statistics, wagging tail effecttime series},
	title = {Seasonal Adjustment as a Source of Spurious Signals},
	journal = {HSE Economic Journal },
	year = {2013},
	volume = {17},
	number = {4},
	pages = {586-616},
	url = {https://ej.hse.ru/en/2013-17-4/109002177.html},
	publisher = {},
	abstract = {The analysis of short-term tendency of economic dynamics can be performed on seasonally adjusted data only. This implies that each time series is to be transformed in two: the seasonal component and the remaining part. The result of such decomposition depends on the specific features of the seasonal adjustment algorithm. Most uncertainty is expected within the neighborhood of crises when the economic indicators are likely to demonstrate substantial changes. Under such circumstances, the seasonal adjustment procedures are likely to generate spurious signals that deteriorate the seasonally adjusted series.In this paper we analyze distortions of seasonally adjusted time series of economic data that appear in the neighborhood of crises. We examined the aberrations caused by sharp level shifts as well as by changes in seasonal pattern and showed that under these circumstances thestandard algorithms of seasonal adjustment can generate spurious signals similar to first signs of a crisis or its second and following waves. We consider these misleading signals from two points of view: first, as an economic historian who operates with long time series of unchanging data; second, as an analyst of short-term dynamics monitoring the data that is subject to revisions.We show that these aberrations can be misleading for understanding of short-run dynamics especially during the first years after a crisis. The identification of the end of a recession and estimation of seasonally adjusted values of observations right after the peak (or bottom) of a fluctuation seem to be the most problematic. Monitoring within this «blind zone» appears to be very complicated. We compared aberrations produced by X-12-ARIMA and TRAMO/SEATS. Some recommendations to soften the distortions are proposed.},
	annote = {The analysis of short-term tendency of economic dynamics can be performed on seasonally adjusted data only. This implies that each time series is to be transformed in two: the seasonal component and the remaining part. The result of such decomposition depends on the specific features of the seasonal adjustment algorithm. Most uncertainty is expected within the neighborhood of crises when the economic indicators are likely to demonstrate substantial changes. Under such circumstances, the seasonal adjustment procedures are likely to generate spurious signals that deteriorate the seasonally adjusted series.In this paper we analyze distortions of seasonally adjusted time series of economic data that appear in the neighborhood of crises. We examined the aberrations caused by sharp level shifts as well as by changes in seasonal pattern and showed that under these circumstances thestandard algorithms of seasonal adjustment can generate spurious signals similar to first signs of a crisis or its second and following waves. We consider these misleading signals from two points of view: first, as an economic historian who operates with long time series of unchanging data; second, as an analyst of short-term dynamics monitoring the data that is subject to revisions.We show that these aberrations can be misleading for understanding of short-run dynamics especially during the first years after a crisis. The identification of the end of a recession and estimation of seasonally adjusted values of observations right after the peak (or bottom) of a fluctuation seem to be the most problematic. Monitoring within this «blind zone» appears to be very complicated. We compared aberrations produced by X-12-ARIMA and TRAMO/SEATS. Some recommendations to soften the distortions are proposed.}
}