Testing for a Unit Root in Time Series Regression

Peter Phillips and Pierre Perron

No 795R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper proposes some new tests for detecting the presence of a unit root in quite general time series models. Our approach is nonparametric with respect to nuisance parameters and thereby allows for a very wide class of weakly dependent and possibly heterogeneously distributed data. The tests accommodate models with a fitted drift and a time trend so that they may be used to discriminate between unit root nonstationarity and stationarity about a deterministic trend. The limiting distributions of the statistics are obtained under both the unit root null and a sequence of local alternatives. The latter noncentral distribution theory yields local asymptotic power functions for the tests and facilitates comparisons with alternative procedures due to Dickey and Fuller. Some simulations are reported which provide evidence on the performance of the new tests in finite samples.

Keywords: Brownian motion; noncentral distributions; weak convergence; nonparametric tests (search for similar items in EconPapers)
Pages: 31 pages
Date: 1986, Revised 1987-09
Note: CFP 706.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (442)

Published in Biometrika (1988), 75(2): 335-346

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Related works:
Journal Article: Time Series Regression with a Unit Root (1987)
Working Paper: Testing for a Unit Root in Time Series Regression (1986)
Software Item: PPUNIT: RATS procedure to perform Phillips-Perron Unit Root test
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