Covariate unit root tests with good size and power

Fossati, Sebastian

doi:10.1016/j.csda.2011.05.019

Cited by 5 publications

(3 citation statements)

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“…Ng and Perron () showed that traditional model selection criteria such as the Bayesian or Akaike information criterion (AIC) satisfy the upper bound condition in Assumption 5 and, hence, can be used to select k . Whereas Pesavento () recommended using the value of k selected by minimizing a modified AIC of the type suggested in Ng and Perron () from an univariate regression on the GLS detrended y t , Fossati () showed that forcing the lead and lag orders in () to be equal results in a small power reduction in the tests that can be avoided by removing this constraint.…”

Section: The Test and Asymptotic Distributionsmentioning

confidence: 99%

Unit root testing with stationary covariates and a structural break in the trend function

Fossati

2013

Journal Time Series Analysis

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The issue of testing for a unit root allowing for a structural break in the trend function is considered. The focus is on the construction of more powerful tests using the information in relevant multi‐variate data sets. The proposed test adopts the generalized least squares detrending approach and uses correlated stationary covariates to improve power. As it is standard in the literature, the break date is treated as unknown. Asymptotic distributions are derived, and a set of asymptotic and finite sample critical values are tabulated. Asymptotic local power functions show that power gains can be large. Finite sample results show that the test exhibits small‐size distortions and power that can be far beyond what is achievable by univariate tests.

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Section: The Test and Asymptotic Distributionsmentioning

confidence: 99%

Unit root testing with stationary covariates and a structural break in the trend function

Fossati

2013

Journal Time Series Analysis

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“…A rich literature has discussed the limitations of the testing approach, see among others (DeJong et al, 1992;Schwert, 1989;Caner and Kilian, 2001;Pitarakis, in press;Fossati, 2012) has proposed refinements and enhancements. Important references are (Perron, 1989;Elliott et al, 1996;Ng and Perron, 2001) for unit roots tests, and (Leybourne and McCabe, 1994) for stationarity tests; see also Harvey (2001) for a review.…”

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confidence: 99%

Characterising economic trends by Bayesian stochastic model specification search

Grassi

Proietti

2014

Computational Statistics & Data Analysis

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“…See, for instance, Schwert (1989), Ng and Perron (1995), Agiakloglou and Newbold (1992), Agiakloglou and Newbold (1996), Elliott et al (1996), Ng and Perron (2001), Del Barrio Castro et al (2011), and Fossati (2012. The consensus is to use data-dependent methods.…”

Section: Introductionmentioning

confidence: 99%

Selecting the Lag Length for the MGLS Unit Root Tests with Structural Change: A Warning Note for Practitioners Based on Simulations

Quineche

Rodríguez

2017

Econometrics

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This is a simulation-based warning note for practitioners who use the M GLS unit root tests in the context of structural change using different selection lag length criteria. With T = 100, we find severe oversize problems when using some criteria, while other criteria produce an undersizing behavior. In view of this dilemma, we do not recommend using these tests. While such behavior tends to disappear when T = 250, it is important to note that most empirical applications use smaller sample sizes such as T = 100 or T = 150. The ADF GLS test does not present an oversizing or undersizing problem. The only disadvantage of the ADF GLS test arises in the presence of MA(1) negative correlation, in which case the M GLS tests are preferable, but in all other cases they are very undersized. When there is a break in the series, selecting the breakpoint using the Supremum method greatly improves the results relative to the Infimum method.

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Covariate unit root tests with good size and power

Abstract: Short excerpts of these working papers may be quoted without explicit permission provided that full credit is given to the source.

Cited by 5 publications

References 20 publications

Unit root testing with stationary covariates and a structural break in the trend function

Unit root testing with stationary covariates and a structural break in the trend function

Characterising economic trends by Bayesian stochastic model specification search

Selecting the Lag Length for the MGLS Unit Root Tests with Structural Change: A Warning Note for Practitioners Based on Simulations

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