Authors: Kley, Tobias
Preuß, Philip
Fryzlewicz, Piotr
Title: Predictive, finite-sample model choice for time series under stationarity and non-stationarity
Language (ISO): en
Abstract: In statistical research there usually exists a choice between structurally simpler or more complex models. We argue that, even if a more complex, locally stationary time series model were true, then a simple, stationary time series model may be advantageous to work with under parameter uncertainty. We present a new model choice methodology, where one of two competing approaches is chosen based on its empirical finite-sample performance with respect to prediction. A rigorous, theoretical analysis of the procedure is provided. As an important side result we prove, for possibly diverging model order, that the localised Yule-Walker estimator is strongly, uniformly consistent under local stationarity. An R package, forecastSNSTS, is provided and used to apply the methodology to financial and meteorological data in empirical examples. We further provide an extensive simulation study and discuss when it is preferable to base forecasts on the more volatile time-varying estimates and when it is advantageous to forecast as if the data were from a stationary process, even though they might not be.
Subject Headings: forecasting
covariance stationarity
Yule-Walker estimate
URI: http://hdl.handle.net/2003/35394
http://dx.doi.org/10.17877/DE290R-17435
Issue Date: 2016
Appears in Collections:Sonderforschungsbereich (SFB) 823

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