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The Exponential Model for the Spectrum of a Time Series: Extensions and Applications

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  • Tommaso Proietti

    ()
    (University of Rome "Tor Vergata")

  • Alessandra Luati

    ()
    (University of Bologna)

Abstract

The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time series processes, also featuring long memory, we discuss likelihood inferences based on the periodogram, for which the estimation of the cepstrum yields a generalized linear model for exponential data with logarithmic link, focusing on the issue of separating the contribution of the long memory component to the log-spectrum. We then propose two extensions. The first deals with replacing the logarithmic link with a more general Box-Cox link, which encompasses also the identity and the inverse links: this enables nesting alternative spectral estimation methods (autoregressive, exponential, etc.) under the same likelihood-based framework. Secondly, we propose a gradient boosting algorithm for the estimation of the log-spectrum and illustrate its potential for distilling the long memory component of the log-spectrum.

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 272.

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Length: 39 pages
Date of creation: 19 Apr 2013
Date of revision: 19 Apr 2013
Handle: RePEc:rtv:ceisrp:272

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Related research

Keywords: Frequency Domain Methods; Generalized linear models; Long Memory; Boosting.;

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References

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  1. Alessandra Luati & Tommaso Proietti & Marco Reale, 2012. "The Variance Profile," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 607-621, June.
  2. Luati, Alessandra & Proietti, Tommaso, 2009. "Hyper-spherical and Elliptical Stochastic Cycles," MPRA Paper 15169, University Library of Munich, Germany.
  3. Donald W.K. Andrews & Patrik Guggenberger, 2000. "A Bias-Reduced Log-Periodogram Regression Estimator for the Long-Memory Parameter," Cowles Foundation Discussion Papers 1263, Cowles Foundation for Research in Economics, Yale University.
  4. Masaki Narukawa & Yasumasa Matsuda, 2011. "Broadband semi‐parametric estimation of long‐memory time series by fractional exponential models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 175-193, 03.
  5. Ori Rosen & Sally Wood & David S. Stoffer, 2012. "AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1575-1589, December.
  6. Hurvich, Clifford M., 2002. "Multistep forecasting of long memory series using fractional exponential models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 167-179.
  7. Tommaso Proietti & Alessandra Luati, 2013. "The Generalised Autocovariance Function," CEIS Research Paper 276, Tor Vergata University, CEIS, revised 30 Apr 2013.
  8. Barndorff-Nielsen, O. & Schou, G., 1973. "On the parametrization of autoregressive models by partial autocorrelations," Journal of Multivariate Analysis, Elsevier, vol. 3(4), pages 408-419, December.
  9. Rosen, Ori & Stoffer, David S. & Wood, Sally, 2009. "Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 249-262.
  10. Koenker, Roger & Yoon, Jungmo, 2009. "Parametric links for binary choice models: A Fisherian-Bayesian colloquy," Journal of Econometrics, Elsevier, vol. 152(2), pages 120-130, October.
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