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Variance stabilizing filters
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Stockholm University, Faculty of Social Sciences, Department of Statistics. National Institute of Economic Research (NIER), Sweden.
2019 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 48, no 24, p. 6155-6168Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2019. Vol. 48, no 24, p. 6155-6168
Keywords [en]
Time series, heteroscedasticity filters, simulation studies, US GDP, Kalman filter
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-169191DOI: 10.1080/03610926.2018.1528369ISI: 000466224200001OAI: oai:DiVA.org:su-169191DiVA, id: diva2:1318854
Available from: 2019-05-28 Created: 2019-05-28 Last updated: 2019-12-08Bibliographically approved

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Gustafsson, OskarStockhammar, Pär
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