A Gaussian Test for Stationarity in a Mixed Frequency Regression for More Power

Tilak Abeysinghe, Gulasekaran Rajaguru

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Unit root tests that are in common use today tend to over-reject the stationarity of economic ratios like the consumption-income ratio or rates like the average tax rate. The meaning of a unit root in such bounded series is not very clear. We use a mixed-frequency regression technique to develop a test for the null hypothesis that a series is stationary. The focus is on regression relationships, not so much on individual series. What is noteworthy about this moving average (MA) unit root test, denoted as z(MA) test, based on a variance-difference, is that, instead of having to deal with non-standard distributions, it takes testing back to normal distribution and offers a way to increase power without having to increase the sample size substantially. Monte Carlo simulations show minimal size distortions even when the AR root is close to unity and the test offers substantial gains in power relative to some popular tests against near-null alternatives in moderate size samples. Applying this test to log of consumption-income ratio of 21 OECD countries shows that the z(MA) test favors stationarity of 15 series, KPSS test 8 series, Johansen test 6 series and ADF test 5 series.
Original languageEnglish
Article number4
Pages (from-to)649-663
Number of pages15
JournalJournal of Data Science
Volume18
Issue number4
DOIs
Publication statusPublished - Oct 2020

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