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Activity Number: 345
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #320278
Title: A New Set of Asymmetric Filters for Real Time Trend-Cycle Estimation
Author(s): Estella Dagum* and Silvia Bianconcini
Companies: University of Bologna and University of Bologna
Keywords: Recession and recovery analysis ; reproducing kernels ; seasonally adjusted data ; Musgrave filters ; time-varying bandwidth selection, ; US economy

For assessing in real time the short-term trend of major economic indicators, official statistical agencies generally rely on asymmetric filters that were developed by Musgrave in 1964. However, the use of the latter introduces revisions as new observations are added to the series and, from a policymaking viewpoint, they are too slow in detecting true turning points. In this paper, we use a reproducing kernel methodology to derive asymmetric filters that converge quickly and monotonically to the corresponding symmetric one. We show theoretically that proposed criteria for time-varying bandwidth selection produce real-time trend-cycle filters to be preferred to the Musgrave filters from the viewpoint of revisions and time delay to detect true turning points. We use a set of leading, coincident and lagging indicators of the US economy to illustrate the potential gains statistical agencies could have by also using our methods in their practice.

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