Predictive, finite-sample model choice for time series under stationarity and non-stationarity

Tobias Kley, Philip Preuss and Piotr Fryzlewicz

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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, in a manner that ensures interpretability. 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.

Keywords: forecasting; Yule-Walker estimate; local stationarity; covariance stationarity (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2019-10-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-hpe and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Electronic Journal of Statistics, 1, October, 2019, 13(2), pp. 3710 - 3774. ISSN: 1935-7524

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:101748

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