 Identifying trend nature in time series using autocorrelation functions and stationarity testsM. Boutahar and M. Royer-Carenzi International Journal of Computational Economics and Econometrics, 2024, vol. 14, issue 1, 1-22 Abstract: Time series non-stationarity can be detected thanks to autocorrelation functions. But trend nature, either deterministic or either stochastic, is not identifiable. Strategies based on Dickey-Fuller unit root-test are appropriate to choose between a linear deterministic trend or a stochastic trend. But all the observed deterministic trends are not linear, and such strategies fail in detecting a quadratic deterministic trend. Being a confounding factor, a quadratic deterministic trend makes a unit root spuriously appear. We provide a new procedure, based on Ouliaris-Park-Phillips unit root test, convenient for time series containing polynomial trends with a degree higher than one. Our approach is assessed based on simulated data. The strategy is finally applied on two real datasets: money stock in USA and on CO2 atmospheric concentration. Compared with Dickey-Fuller diagnosis, our strategy provides the model with the best performances. Keywords: time series; stationarity; autocorrelation functions; unit root tests; Dickey-Fuller; KPSS; OPP test; trend detection; deterministic or stochastic trend; spurious unit root. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:14:y:2024:i:1:p:1-22 Access Statistics for this article More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd |
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