Wavelet Analysis for Temporal Disaggregation

31 Pages Posted: 29 Oct 2018

Date Written: October 29, 2018

Abstract

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 fi rst 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 flow and stock variables, and it outperforms other standard methods, as Stram and Wei or low pass interpolation when the series of interest is volatile.

Keywords: wavelet, temporal disaggregation, sector financial accounts

JEL Classification: C10, C65, C32, E32

Suggested Citation

Perricone, Chiara, Wavelet Analysis for Temporal Disaggregation (October 29, 2018). CEIS Working Paper No. 444. Available at SSRN: https://ssrn.com/abstract=3274721 or http://dx.doi.org/10.2139/ssrn.3274721

Chiara Perricone (Contact Author)

University of Rome Tor Vergata ( email )

via columbia 2
rome, Lazio 00133
Italy

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