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A POWERFUL TEST OF THE AUTOREGRESSIVE UNIT ROOT HYPOTHESIS BASED ON A TUNING PARAMETER FREE STATISTIC

Published online by Cambridge University Press:  01 December 2009

Morten Ørregaard Nielsen*
Affiliation:
Queen’s University and CREATES
*
*Address correspondence to Morten Ørregaard Nielsen, Department of Economics, Dunning Hall, room 307, 94 University Avenue, Queen’s University, Kingston, Ontario, K7L 3N6, Canada; e-mail: [email protected].

Abstract

This paper presents a family of simple nonparametric unit root tests indexed by one parameter, d, and containing the Breitung (2002, Journal of Econometrics 108, 342–363) test as the special case d = 1. It is shown that (a) each member of the family with d > 0 is consistent, (b) the asymptotic distribution depends on d and thus reflects the parameter chosen to implement the test, and (c) because the asymptotic distribution depends on d and the test remains consistent for all d > 0, it is possible to analyze the power of the test for different values of d. The usual Phillips–Perron and Dickey–Fuller type tests are indexed by bandwidth, lag length, etc., but have none of these three properties.

It is shown that members of the family with d < 1 have higher asymptotic local power than the Breitung (2002) test, and when d is small the asymptotic local power of the proposed nonparametric test is relatively close to the parametric power envelope, particularly in the case with a linear time trend. Furthermore, generalized least squares (GLS) detrending is shown to improve power when d is small, which is not the case for the Breitung (2002) test. Simulations demonstrate that when applying a sieve bootstrap procedure, the proposed variance ratio test has very good size properties, with finite-sample power that is higher than that of the Breitung (2002) test and even rivals the (nearly) optimal parametric GLS detrended augmented Dickey–Fuller test with lag length chosen by an information criterion.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Agiakloglou, C. & Newbold, P. (1996) The balance between size and power in Dickey-Fuller tests with data-dependent rules for the choice of truncation lag. Economics Letters 52, 229234.CrossRefGoogle Scholar
Akonom, J. & Gourieroux, C. (1987) A Functional Central Limit Theorem for Fractional Processes. Technical report 8801, CEPREMAP, Paris.Google Scholar
Baillie, R.T. (1996) Long memory processes and fractional integration in econometrics. Journal of Econometrics 73, 559.CrossRefGoogle Scholar
Basawa, I.V., Mallik, A.K., McCormick, W.P., Reeves, J.H., & Taylor, R.L. (1991) Bootstrapping unstable first-order autoregressive processes. Annals of Statistics 19, 10981101.CrossRefGoogle Scholar
Breitung, J. (2002) Nonparametric tests for unit roots and cointegration. Journal of Econometrics 108, 342363.CrossRefGoogle Scholar
Breitung, J. & Taylor, A.M.R. (2003) Corrigendum to “Nonparametric tests for unit roots and cointegration [J. Econom. 108 (2002), 343–363].” Journal of Econometrics 117, 401404.CrossRefGoogle Scholar
Buchmann, B. & Chan, N.H. (2007) Asymptotic theory of least squares estimators for nearly unstable processes under strong dependence. Annals of Statistics 35, 20012017.CrossRefGoogle Scholar
Chan, N.H. & Wei, C.Z. (1987) Asymptotic inference for nearly nonstationary AR(1) processes. Annals of Statistics 15, 10501063.CrossRefGoogle Scholar
Chang, Y. & Park, J.Y. (2003) A sieve bootstrap for the test of a unit root. Journal of Time Series Analysis 24, 379400.CrossRefGoogle Scholar
Davidson, J. & de Jong, R.M. (2000) The functional central limit theorem and weak convergence to stochastic integrals II: Fractionally integrated processes. Econometric Theory 16, 643666.CrossRefGoogle Scholar
Dickey, D.A. & Fuller, W.A. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427431.Google Scholar
Dickey, D.A. & Fuller, W.A. (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 10571072.CrossRefGoogle Scholar
Doornik, J.A. (2006) Ox: An Object-Oriented Matrix Programming Language. Timberlake Consultants Press.Google Scholar
Elliott, G., Rothenberg, T.J., & Stock, J.H. (1996) Efficient tests for an autoregressive unit root. Econometrica 64, 813836.CrossRefGoogle Scholar
Franses, P.H. & Haldrup, N. (1994) The effects of additive outliers on tests for unit roots and cointegration. Journal of Business & Economic Statistics 12, 471478.Google Scholar
Haldrup, N. & Jansson, M. (2006) Improving size and power in unit root testing. In Mills, T.C. & Patterson, K. (eds.), Palgrave Handbook of Econometrics, vol. 1, pp. 252277. Palgrave Macmillan.Google Scholar
Hualde, J. (2007) Estimation of Long-Run Parameters in Unbalanced Cointegration. Working paper, Universidad de Navarra.Google Scholar
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., & Shin, Y. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics 54, 159178.CrossRefGoogle Scholar
Leybourne, S.J. & Newbold, P. (1999a) The behaviour of Dickey-Fuller and Phillips-Perron tests under the alternative hypothesis. Econometrics Journal 2, 92106.CrossRefGoogle Scholar
Leybourne, S.J. & Newbold, P. (1999b) On the size properties of Phillips-Perron tests. Journal of Time Series Analysis 20, 5161CrossRefGoogle Scholar
Marinucci, D. & Robinson, P.M. (2000) Weak convergence of multivariate fractional processes. Stochastic Processes and Their Applications 86, 103120.CrossRefGoogle Scholar
Müller, U.K. (2007) A theory of robust long-run variance estimation. Journal of Econometrics 141, 13311352.CrossRefGoogle Scholar
Müller, U.K. (2008) The impossibility of consistent discrimination between I(0) and I(1) processes. Econometric Theory 24, 616630.CrossRefGoogle Scholar
Müller, U.K. & Elliott, G. (2003) Tests for unit roots and the initial condition. Econometrica 71, 12691286.CrossRefGoogle Scholar
Ng, S. & Perron, P. (2001) Lag length selection and the construction of unit root tests with good size and power. Econometrica 69, 15191554.CrossRefGoogle Scholar
Nielsen, M.Ø. (2008) A Powerful Tuning Parameter Free Test of the Autoregressive Unit Root Hypothesis. QED Working paper 1175, Queen’s University.CrossRefGoogle Scholar
Park, J.Y. (1990) Testing for unit roots and cointegration by variable addition. In Fomby, T.B. & Rhodes, G.F., (eds.), Advances in Econometrics, vol. 8: Co-Integration, Spurious Regressions, and Unit Roots, pp. 107133. Elsevier Science.Google Scholar
Park, J.Y. & Choi, B. (1988) A New Approach to Testing for a Unit Root. CAE Working paper 88–23, Cornell University.Google Scholar
Perron, P. & Qu, Z. (2007) A simple modification to improve the finite sample properties of Ng and Perron’s unit root tests. Economics Letters 94, 1219.CrossRefGoogle Scholar
Phillips, P.C.B. (1987a) Time series regression with a unit root. Econometrica 55, 277301.CrossRefGoogle Scholar
Phillips, P.C.B. (1987b) Towards a unified asymptotic theory for autoregression. Biometrika 74, 535547.CrossRefGoogle Scholar
Phillips, P.C.B. & Perron, P. (1988) Testing for a unit root in time series regression. Biometrika 75, 335346.CrossRefGoogle Scholar
Phillips, P.C.B. & Solo, V. (1992) Asymptotics for linear processes. Annals of Statistics 20, 9711001.CrossRefGoogle Scholar
Phillips, P.C.B. & Xiao, Z. (1998) A primer on unit root testing. Journal of Economic Surveys 12, 423469.CrossRefGoogle Scholar
Robinson, P.M. (1994) Efficient tests of nonstationary hypotheses. Journal of the American Statistical Association 89, 14201437.CrossRefGoogle Scholar
Robinson, P.M. (2003) Long-memory time series. In Robinson, P.M. (ed.), Time Series with Long Memory, pp. 432. Oxford University Press.CrossRefGoogle Scholar
Shin, Y. & Schmidt, P. (1992) The KPSS stationarity test as a unit root test. Economics Letters 38, 387392.CrossRefGoogle Scholar
Stock, J.H. (1994) Unit roots, structural breaks and trends. In Engle, R.F. & McFadden, D.L. (eds.), Handbook of Econometrics, vol. 4, pp. 28432915. North-Holland.Google Scholar
Tanaka, K. (1999) The nonstationary fractional unit root. Econometric Theory 15, 549582.CrossRefGoogle Scholar
Taylor, A.M.R. (2005) Variance ratio tests of the seasonal unit root hypothesis. Journal of Econometrics 124, 3354.CrossRefGoogle Scholar
Vogelsang, T.J. (1998a) Testing for a shift in mean without having to estimate serial-correlation parameters. Journal of Business & Economic Statistics 16, 7380.Google Scholar
Vogelsang, T.J. (1998b) Trend function hypothesis testing in the presence of serial correlation. Econometrica 66, 123148.CrossRefGoogle Scholar
White, H. (1984) Asymptotic Theory for Econometricians. Academic Press.Google Scholar