Variance stabilizing filters#

Oskar Gustafsson and Pär Stockhammar

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 24, 6155-6168

Abstract: 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.

Date: 2019
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DOI: 10.1080/03610926.2018.1528369

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