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Band Spectral Estimation for Signal Extraction

Author

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  • Tommaso Proietti

    (SEFEMEQ, Universita’ di Roma "Tor Vergata")

Abstract

The paper evaluates the potential of band spectral estimation for extracting signals in economic time series. Two situations are considered. The first deals with trend extraction when the original data have been permanently altered by routine operations, such as prefiltering, temporal aggregation and disaggregation, and seasonal adjustment, which modify the high frequencies properties of economic time series. The second is when the measurement model is only partially specified, in that it aims at fitting the series in a particular frequency range, e.g. at interpreting the long run behaviour. These issues are illustrated with reference to a simple structural model, namely the random walk plus noise model.

Suggested Citation

  • Tommaso Proietti, 2007. "Band Spectral Estimation for Signal Extraction," CEIS Research Paper 104, Tor Vergata University, CEIS.
    • Handle: RePEc:rtv:ceisrp:104
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    References listed on IDEAS

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    6. Christiano, Lawrence J. & Vigfusson, Robert J., 2003. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 789-815, May.
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    11. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
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    Cited by:

    1. D.S.G. Pollock, 2017. "Stochastic processes of limited frequency and the effects of oversampling," Discussion Papers in Economics 17/03, Division of Economics, School of Business, University of Leicester.
    2. D.S.G. Pollock, 2010. "Oversampling of stochastic processes," Working Papers 44, Department of Applied Econometrics, Warsaw School of Economics.
    3. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).
    4. Pollock, D.S.G., 2018. "Stochastic processes of limited frequency and the effects of oversampling," Econometrics and Statistics, Elsevier, vol. 7(C), pages 18-29.
    5. Shi, Wendong & Sun, Jingwei, 2016. "Aggregation and long-memory: An analysis based on the discrete Fourier transform," Economic Modelling, Elsevier, vol. 53(C), pages 470-476.

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    More about this item

    Keywords

    Temporal Aggregation; Seasonal Adjustment; Trend Component; Frequency Domain.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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