Wavelet Analysis for Temporal Disaggregation

Perricone Chiara
CEIS Research Paper
A problem often faced by economic researchers is the interpolation or distribution of economic time series observed at low frequency into compatible higher frequency data. A method based on wavelet analysis is presented to temporal disaggregate time series. A standard `plausible' method is applied, not to the original time series, but to the smooth components resulting from a discrete wavelet transformation. This first step generates a smoothed component at the desired frequency. Subsequently, a noisy component is added to the smooth series to enforce the natural constraint of the series. The method is applied to national accounts for Euro Area, to study both ow and stock variables, and it outperforms other standard methods, as Stram and Wei or low pass interpolation when the series of interest is volatile.

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Number: 444
Keywords: wavelet, temporal disaggregation, sector financial accounts
JEL codes: C10, C65, C32, E32
Volume: 16
Issue: 7
Date: Monday, October 29, 2018
Revision Date: Monday, October 29, 2018