Wavelet analysis for temporal disaggregation

Chiara Perricone ()

No 444, CEIS Research Paper from Tor Vergata University, CEIS

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 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.

Keywords: wavelet; temporal disaggregation; sector financial accounts (search for similar items in EconPapers)
JEL-codes: C10 C32 C65 E32 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2018-10-29, Revised 2018-10-29
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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