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Linear dynamic harmonic regression

Bujosa, Marcos; Garcia-Ferrer, Antonio; Young, Peter C

Description

Among the alternative unobserved components formulations within the stochastic state space setting, the dynamic harmonic regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain AutoRegressive moving average (ARMA) representations for the DHR components under a generalized random walk setting for the associated stochastic parameters; a setting that includes...[Show more]

dc.contributor.authorBujosa, Marcos
dc.contributor.authorGarcia-Ferrer, Antonio
dc.contributor.authorYoung, Peter C
dc.date.accessioned2015-12-10T22:13:45Z
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/1885/49895
dc.description.abstractAmong the alternative unobserved components formulations within the stochastic state space setting, the dynamic harmonic regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain AutoRegressive moving average (ARMA) representations for the DHR components under a generalized random walk setting for the associated stochastic parameters; a setting that includes several well-known random walk models as special cases. Later, these theoretical results are used to derive an alternative algorithm, based on optimization in the frequency domain, for the identification and estimation of DHR models. The main advantages of this algorithm are linearity, fast computational speed, avoidance of some numerical issues, and automatic identification of the DHR model. The signal extraction performance of the algorithm is evaluated using empirical applications and comprehensive Monte Carlo simulation analysis.
dc.publisherElsevier
dc.sourceComputational Statistics and Data Analysis
dc.subjectKeywords: Algorithms; Harmonic analysis; Monte Carlo methods; Numerical methods; Optimization; Parameter estimation; Random processes; Signal processing; State space methods; Time series analysis; Dynamic harmonic regression; Ordinary least squares; Spectral fittin Dynamic harmonic regression; Ordinary least squares; Spectral fitting; Unobserved component models
dc.titleLinear dynamic harmonic regression
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume52
dc.date.issued2007
local.identifier.absfor070504 - Forestry Management and Environment
local.identifier.ariespublicationu3379551xPUB194
local.type.statusPublished Version
local.contributor.affiliationBujosa, Marcos, Universidad Complutense de Madrid
local.contributor.affiliationGarcia-Ferrer, Antonio, Universidad Autonoma de Madrid
local.contributor.affiliationYoung, Peter C, College of Medicine, Biology and Environment, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue2
local.bibliographicCitation.startpage999
local.bibliographicCitation.lastpage1024
local.identifier.doi10.1016/j.csda.2007.07.008
local.identifier.absseo960999 - Land and Water Management of environments not elsewhere classified
dc.date.updated2015-12-09T07:59:21Z
local.identifier.scopusID2-s2.0-35148881187
CollectionsANU Research Publications

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