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Activity Number: 588
Type: Topic Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #307680
Title: Real-Time Detection of Trend-Cycle Turning Points
Author(s): Estela Dagum*+ and Silvia Bianconcini
Companies: University of Bologna and University of Bologna
Keywords: Asymmetric filters ; nonparametric methods ; reproducing kernel Hilbert space ; bandwidth selection ; revisions ; spectral properties
Abstract:

For real time analysis, official statistical agencies analyze trend-cycle estimates, generally derived using asymmetric moving average techniques. But the use of the latter introduces revisions as new observations are added to the series, and delays in detecting true turning points. In this paper, we consider a reproducing kernel representation of commonly applied nonparametric trend-cycle predictors to derive asymmetric filters that monotonically converge to the corresponding symmetric one. We consider three specific criteria of bandwidth selection,(1)minimization of the transfer function which implies an optimal compromise between reducing revisions and phase shift;(2)minimization of the gain function which implies revisions reduction, and (3) minimization of the phase shift function which implies reduction of the time lag to detect a true turning point. We can obtain real time trend-cycle predictors with the properties of either minimization of revision, or fast detection of turning points or an optimal compromise between these two. The behavior of the proposed procedure is illustrated with real series mainly used to identify and predict true macroeconomic turning points.


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