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A note on the pseudo-spectra and the pseudo-covariance generating functions of ARMA processes

dc.contributor.authorBujosa Brun, Andrés
dc.contributor.authorBujosa Brun, Marcos
dc.contributor.authorGarcía Ferrer , Antonio
dc.date.accessioned2023-06-21T01:45:30Z
dc.date.available2023-06-21T01:45:30Z
dc.date.issued2002-07
dc.description.abstractAlthough the spectral analysis of stationary stochastic processes has solid mathematical foundations, this is not the case for non-stationary stochastic processes. In this paper, the algebraic foundations of the spectral analysis of non-stationary ARMA processes are established. For this purpose the Fourier Transform is extended to the field of fractions of polynomials. Then, the Extended Fourier Transform pair pseudo-covariance generating function / pseudo-spectrum, analogous to the Fourier Transform pair covariance generating function / spectrum, is defined. The new transform pair is well defined for stationary and non-stationary ARMA processes. This new approach can be viewed as an extension of the classical spectral analysis. It is shown that the frequency domain has some additional algebraic advantages over the time domain.
dc.description.facultyFac. de Ciencias Económicas y Empresariales
dc.description.facultyInstituto Complutense de Análisis Económico (ICAE)
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/7653
dc.identifier.relatedurlhttps://www.ucm.es/icae
dc.identifier.urihttps://hdl.handle.net/20.500.14352/64493
dc.issue.number03
dc.language.isoeng
dc.page.total18
dc.publication.placeMadrid
dc.publisherInstituto Complutense de Análisis Económico. Universidad Complutense de Madrid
dc.relation.ispartofseriesDocumentos de trabajo del Instituto Complutense de Análisis Económico (ICAE)
dc.rights.accessRightsopen access
dc.subject.keywordFourier Transform
dc.subject.ucmEconometría (Economía)
dc.subject.unesco5302 Econometría
dc.titleA note on the pseudo-spectra and the pseudo-covariance generating functions of ARMA processes
dc.typetechnical report
dc.volume.number2002
dcterms.referencesBrockwell, P. J., and R. A. Davis (1987): Time Series: Theory and Methods, Springer series in Statistics. Springer-Verlag, New York. Bujosa, M., A. Garc´ıa-Ferrer, and P. C. Young (2002): “An ARMA representation of unobserved component models under generalized random walk specifications: new algorithms and examples,”mimeo. Caines, P. E. (1988): Linear Stochastic Systems, Wiley series in probability and mathematical statistics. John Wiley & Sons, Inc., New York. Godement, R. (1974): ´ Algebra. Editorial Tecnos, Madrid, first edn. Harvey, A. (1989): Forecasting Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge, first edn. Hatanaka, M., and M. Suzuki (1967): “A theory of the Pseudosprectrum and Its Application to Nonstationary Dynamic Ecomometric Models,” in Essays in Mathematical Economics. In Honor of Oskar Morgenstern, ed. by M. Shubik, chap. 26, pp. 443–446. Princeton University Press, Princeton, New Jersey. Luenberger, D. G. (1968): Optimization by vector space methods, Series in decision and control. John Wiley & Sons, Inc., New York. Priestley, M. P. (1981): Spectral Analysis and Time Series, Probability and Mathematical Statistics. Academic Press, London, first edn. Young, P. C., D. Pedregal, and W. Tych (1999): “Dynamic Harmonic Regression,” Journal of Forecasting, 18, 369–394.
dspace.entity.typePublication
relation.isAuthorOfPublication3e6ecbfe-83ad-404d-8f47-b6e76491c702
relation.isAuthorOfPublication.latestForDiscovery3e6ecbfe-83ad-404d-8f47-b6e76491c702

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