The Exponential Model for the Spectrum of a Time Series: Extensions and Applications
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.
|Date of creation:||19 Apr 2013|
|Date of revision:||19 Apr 2013|
|Contact details of provider:|| Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma|
Web page: http://www.ceistorvergata.it
More information through EDIRC
|Order Information:|| Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma|
Web: http://www.ceistorvergata.it Email:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Donald W. K. Andrews & Patrik Guggenberger, 2003.
"A Bias--Reduced Log--Periodogram Regression Estimator for the Long--Memory Parameter,"
Econometric Society, vol. 71(2), pages 675-712, March.
- 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.
- Tom Doan, "undated". "AGFRACTD: RATS procedure to compute Andrews-Guggenberger estimate of fractional difference," Statistical Software Components RTS00005, Boston College Department of Economics.
- 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.
- 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.
- Luati, Alessandra & Proietti, Tommaso & Reale, Marco, 2011. "The Variance Profile," MPRA Paper 30378, University Library of Munich, Germany.
- Proietti, Tommaso & Luati, Alessandra, 2015. "The generalised autocovariance function," Journal of Econometrics, Elsevier, vol. 186(1), pages 245-257.
- Tommaso, Proietti & Alessandra, Luati, 2012. "The Generalised Autocovariance Function," MPRA Paper 43711, University Library of Munich, Germany.
- Tommaso Proietti & Alessandra Luati, 2013. "The Generalised Autocovariance Function," CEIS Research Paper 276, Tor Vergata University, CEIS, revised 30 Apr 2013.
- Alessandra Luati & Tommaso Proietti, 2010. "Hyper-spherical and elliptical stochastic cycles," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(3), pages 169-181, May.
- Luati, Alessandra & Proietti, Tommaso, 2009. "Hyper-spherical and Elliptical Stochastic Cycles," MPRA Paper 15169, University Library of Munich, Germany.
- 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.
- 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, March.
- 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.
- 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.
- 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. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:rtv:ceisrp:272. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Barbara Piazzi)
If references are entirely missing, you can add them using this form.