Univariate Unit Root Tests Perform Poorly When Data Are Cointegrated

W. Reed ()

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: This note demonstrates that unit root tests can suffer from inflated Type I error rates when data are cointegrated. Results from Monte Carlo simulations show that three commonly used unit root tests – the ADF, Phillips-Perron, and DF-GLS tests – frequently overreject the true null of a unit root for at least one of the cointegrated variables in reasonably sized samples. While the addition of lagged differenced (LD) terms can sometimes eliminate the size distortion, standard diagnostics such as (i) testing for serial correlation in the residuals and (ii) using information criteria to select lags are unable to identify the appropriate number of terms.

Keywords: Unit root testing; cointegration; DF-GLS test; Augmented Dickey-Fuller test; Phillips-Perron test; Monte Carlo; simulation (search for similar items in EconPapers)
JEL-codes: C18 C22 C32 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2016-01-22
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:16/01

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