Generalised Linear Spectral Models
In this chapter we consider a class of parametric spectrum estimators based on a generalized linear model for exponential random variables with power link. The power transformation of the spectrum of a stationary process can be expanded in a Fourier series, with the coefficients representing generalised autocovariances. Direct Whittle estimation of the coefficients is generally unfeasible, as they are subject to constraints (the autocovariances need to be a positive semidefinite sequence). The problem can be overcome by using an ARMA representation for the power transformation of the spectrum. Estimation is carried out by maximising the Whittle likelihood, whereas the selection of a spectral model, as a function of the power transformation parameter and the ARMA orders, can be carried out by information criteria. The proposed methods are applied to the estimation of the inverse autocorrelation function and the related problem of selecting the optimal interpolator, and for the identification of spectral peaks. More generally, they can be applied to spectral estimation with possibly misspecified models.
|Date of creation:||03 Oct 2013|
|Date of revision:||03 Oct 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.:
- Harvey, A.C. & Trimbur, T.M., 2001.
"General Model-based Filters for Extracting Cycles and Trends in Economic Time Series,"
Cambridge Working Papers in Economics
0113, Faculty of Economics, University of Cambridge.
- Andrew C. Harvey & Thomas M. Trimbur, 2003. "General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 244-255, May.
- Harvey, A C, 1985. "Trends and Cycles in Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 216-27, June.
- Tommaso Proietti & Alessandra Luati, 2013.
"The Generalised Autocovariance Function,"
CEIS Research Paper
276, Tor Vergata University, CEIS, revised 30 Apr 2013.
- Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
- Tommaso Proietti, 2006. "Trend-Cycle Decompositions with Correlated Components," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 61-84.
- Alessandra Luati & Tommaso Proietti, 2010.
"Hyper-spherical and elliptical stochastic cycles,"
Journal of Time Series Analysis,
Wiley Blackwell, vol. 31(3), pages 169-181, 05.
- Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-47, July-Sept.
When requesting a correction, please mention this item's handle: RePEc:rtv:ceisrp:290. 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.