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dc.contributor.authorHerwartz, Helmuten_US
dc.contributor.authorSiedenburg, Florianen_US
dc.description.abstractA novel simulation based approach to unit root testing is proposed in this paper. The test is constructed from the distinct orders in probability of the OLS parameter estimates obtained from a spurious and an unbalanced regression, respectively. While the parameter estimate from a regression of two integrated and uncorrelated time series is of order Op(1), the estimate is of order Op(T-1) if the dependent variable is stationary. The test statistic is constructed as an inter quantile range from the empirical distribution obtained from regressing the standardized data sufficiently often on controlled random walks. GLS detrending (Elliott et al, 1996) and spectral density variance estimators (Perron and Ng, 1998) are applied to account for deterministic terms and residual autocorrelation in the data. A Monte Carlo study confirms that the proposed test has favorable empirical size properties and is powerful in local-to-unity neighborhoods. Testing for PPP for a sample of G6 economies, the proposed test yields results in favor of PPP for half of the sample economies while benchmark tests obtain at most one rejection of the random walk null hypothesis.en_US
dc.publisher|aKiel University, Department of Economics |cKielen_US
dc.relation.ispartofseries|aEconomics Working Paper |x2009-06en_US
dc.subject.keywordUnit root testsen_US
dc.subject.keywordsimulation based testen_US
dc.subject.keywordsimulation studyen_US
dc.subject.keywordGLS detrendingen_US
dc.titleA new approach to unit root testingen_US
dc.type|aWorking Paperen_US

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