Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/114170
Authors: 
Reed, W. Robert
Year of Publication: 
2015
Series/Report no.: 
Economics Discussion Papers 2015-57
Abstract: 
This paper 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. The reason for this overrejection is that unit root tests, designed for random walk data, are often misspecified when data are cointegrated. While the addition of lagged differenced (LD) terms can eliminate the size distortion, this "success" is spurious, driven by collinearity between the lagged dependent variable and the LD explanatory variables. Accordingly, standard diagnostics such as (i) testing for serial correlation in the residuals and (ii) using information criteria to select among different lag specifications are futile. The implication of these results is that researchers should be conservative in the weight they attach to individual unit root tests when determining whether data are cointegrated.
Subjects: 
unit root testing
cointegration
DF-GLS test
augmented Dickey-Fuller test
Phillips-Perron test
simulation
JEL: 
C32
C22
C18
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Working Paper

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