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Wavelet analysis for temporal disaggregation

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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.

Suggested Citation

  • Chiara Perricone, 2018. "Wavelet analysis for temporal disaggregation," CEIS Research Paper 444, Tor Vergata University, CEIS, revised 29 Oct 2018.
    • Handle: RePEc:rtv:ceisrp:444
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    References listed on IDEAS

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    More about this item

    Keywords

    wavelet; temporal disaggregation; sector financial accounts;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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