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Variance stabilizing filters
Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen. National Institute of Economic Research (NIER), Sweden.
2019 (engelsk)Inngår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 48, nr 24, s. 6155-6168Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this paper new filters for removing unspecified form of heteroscedasticity are proposed. The filters build on the assumption that the variance of a pre-whitened time series can be viewed as a latent stochastic process by its own. This makes the filters flexible and useful in many situations. A simulation study shows that removing heteroscedasticity before fitting a model leads to efficiency gains and bias reductions when estimating the parameters of ARMA models. A real data study shows that pre-filtering can increase the forecasting precision of quarterly US GDP growth.

sted, utgiver, år, opplag, sider
2019. Vol. 48, nr 24, s. 6155-6168
Emneord [en]
Time series, heteroscedasticity filters, simulation studies, US GDP, Kalman filter
HSV kategori
Forskningsprogram
statistik
Identifikatorer
URN: urn:nbn:se:su:diva-169191DOI: 10.1080/03610926.2018.1528369ISI: 000466224200001OAI: oai:DiVA.org:su-169191DiVA, id: diva2:1318854
Tilgjengelig fra: 2019-05-28 Laget: 2019-05-28 Sist oppdatert: 2019-12-08bibliografisk kontrollert

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