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Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation pp 263–278Cite as

The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction

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Abstract

Asymmetric nonparametric trend-cycle filters obtained with local time-varying bandwidth parameters via the Reproducing Kernel Hilbert Space (RKHS) methodology reduce significantly revisions and turning point detection respect to the currently used by statistical agencies. The best choice of local time-varying bandwidth is the one obtained by minimizing the distance between the gain functions of the RKHS asymmetric and the symmetric filter to which it must converge. Since the input to these kernel filters is seasonally adjusted series, it is important to evaluate the impact that the seasonal adjustment method can have. The purpose of this chapter is to assess the effects of the seasonal adjustment methods when the real time trend is predicted with such nonparametric kernel filters. The seasonal adjustments compared are the two officially adopted by statistical agencies: X12ARIMA and TRAMO-SEATS applied to a sample of US leading, coincident, and lagging indicators.

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Authors and Affiliations

  1. Department of Statistical Sciences, University of Bologna, Bologna, Italy

    Estela Bee Dagum & Silvia Bianconcini

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  1. Estela Bee Dagum
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  2. Silvia Bianconcini
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Bee Dagum, E., Bianconcini, S. (2016). The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction. In: Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. Statistics for Social and Behavioral Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-31822-6_11

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