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Testing for long memory in the presence of a general trend

Published online by Cambridge University Press:  14 July 2016

Liudas Giraitis*
Affiliation:
London School of Economics
Piotr Kokoszka*
Affiliation:
University of Liverpool
Remigijus Leipus*
Affiliation:
Vilnius University
*
Postal address: Department of Economics, London School of Economics, Houghton Street, London WC2A 2AE, UK. Email address: l.giraitis@lse.ac.uk
∗∗ Postal address: Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.
∗∗∗ Postal address: Department of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius 2600, Lithuania.

Abstract

The paper studies the impact of a broadly understood trend, which includes a change point in mean and monotonic trends studied by Bhattacharya et al. (1983), on the asymptotic behaviour of a class of tests designed to detect long memory in a stationary sequence. Our results pertain to a family of tests which are similar to Lo's (1991) modified R/S test. We show that both long memory and nonstationarity (presence of trend or change points) can lead to rejection of the null hypothesis of short memory, so that further testing is needed to discriminate between long memory and some forms of nonstationarity. We provide quantitative description of trends which do or do not fool the R/S-type long memory tests. We show, in particular, that a shift in mean of a magnitude larger than N, where N is the sample size, affects the asymptotic size of the tests, whereas smaller shifts do not do so.

Type
Research Papers
Copyright
Copyright © by the Applied Probability Trust 2001 

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References

Anderson, T. W. (1971). The Statistical Analysis of Time Series. John Wiley, New York.Google Scholar
Baum, C. F., Barkoulas, J. T., and Caglayan, M. (1999). Long memory or structural breaks: can either explain nonstationary real exchange rates under the current float? J. Internat. Financial Markets, Institutions, Money 9, 359376.CrossRefGoogle Scholar
Bhattacharya, R. N., Gupta, V. K., and Waymire, E. (1983). The Hurst effect under trends. J. Appl. Prob. 20, 649662.CrossRefGoogle Scholar
Billingsley, P. (1968). Convergence of Probability Measures. John Wiley, New York.Google Scholar
Bos, C. S., Franses, P. H., and Ooms, M. (1999). Long memory and level shifts: re-analyzing inflation rates. Empirical Econom. 24, 427449.CrossRefGoogle Scholar
Bühlmann, P. (1996). Locally adaptive lag-window spectral estimation. J. Time Ser. Anal. 17, 123148.CrossRefGoogle Scholar
Campbell, J. T., Lo, A. W, and MacKinley, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.CrossRefGoogle Scholar
Davydov, Y. A. (1970). The invariance principle for stationary processes. Theory Prob. Appl. 15, 487498.CrossRefGoogle Scholar
Diebold, F. X., and Inoue, A. (2001). Long memory and structural change. J. Econometrics 105, 131159. Long memory and structural change. Preprint, New York University. Available at http://www.stern.nyu.edu/~fdiebold/papers/papers.html.CrossRefGoogle Scholar
Giraitis, L., and Robinson, P. (2001). Whittle estimation procedure of ARCH models. Econometric Theory 17, 608631.CrossRefGoogle Scholar
Giraitis, L., and Surgailis, D. (1986). Multivariate Appell polynomials and the central limit theorem. In Dependence in Probability and Statistics, eds Eberlein, E. and Taqqu, M. S., Birkhäuser, Boston, pp. 2171.CrossRefGoogle Scholar
Giraitis, L., Kokoszka, P., and Leipus, R. (2000a). Rescaled variance and related tests for long memory in volatility and levels. Preprint, Utah State University. Available at http://www.math.usu.edu/~piotr/research.html.Google Scholar
Giraitis, L., Kokoszka, P., and Leipus, R. (2000b). Stationary ARCH models: dependence structure and Central Limit Theorem. Econometric Theory 16, 322.CrossRefGoogle Scholar
Giraitis, L., Robinson, P., and Surgailis, D. (2000). A model for long memory conditional heteroskedasticity. Ann. Appl. Prob. 10, 10021024.CrossRefGoogle Scholar
Granger, C. W. J., and Hyung, N. (1999). Occasional structural breaks and long memory. Discussion Paper 99-14, Department of Economics, University of California, San Diego.Google Scholar
Hall, P. (1997). Defining and measuring long-range dependence. In Nonlinear Dynamics and Time Series (Fields Inst. Commun. 11), eds Cutler, C. D. and Kaplan, D. T., American Mathematical Society, Providence, RI, pp. 153160.Google Scholar
Heyde, C. C., and Dai, W. (1996). On the robustness to small trends of estimation based on the smoothed periodogram. J. Time Ser. Anal. 17, 141150.CrossRefGoogle Scholar
Heyde, C. C., and Yang, Y. (1997). On defining long-range dependence. J. Appl. Prob. 34, 939944.CrossRefGoogle Scholar
Hurst, H. (1951). Long term storage capacity of reservoirs. Trans. Amer. Soc. Civil Eng. 116, 770799.CrossRefGoogle Scholar
Ibragimov, I. A. and Linnik, Yu. V. (1971). Independent and Stationary Sequences of Random Variables. Wolters–Noordhoff, Groningen.Google Scholar
Künsch, H. (1986). Discrimination between monotonic trends and long-range dependence. J. Appl. Prob. 23, 10251030.CrossRefGoogle Scholar
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., and 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? J. Econometrics 54, 159178.CrossRefGoogle Scholar
Lo, A. (1991). Long-term memory in stock market prices. Econometrica 59, 12791313.CrossRefGoogle Scholar
Lobato, I. N., and Savin, N. E. (1998). Real and spurious long-memory properties of stock-market data (with comments). J. Business Econom. Statist. 16, 261283.Google Scholar
Mandelbrot, B. B. (1972). Statistical methodology for non-periodic cycles: from the covariance to R/S analysis. Ann. Econom. Social Measurement 1, 259290.Google Scholar
Mandelbrot, B. B. (1975). Limit theorems of the self-normalized range for weakly and strongly dependent processes. Z. Wahrscheinlichkeitsch. 31, 271285.CrossRefGoogle Scholar
Mandelbrot, B. B., and Taqqu, M. S. (1979). Robust R/S analysis of long run serial correlation. In Proc. 42nd Session Internat. Statist. Inst., Manila, Vol. 2 (Bull. Internat. Statist. Inst. 48), pp. 6999.Google Scholar
Mandelbrot, B. B., and Wallis, J. M. (1969). Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Res., 5, 967988.CrossRefGoogle Scholar
Mikosch, T. and Stărică, C. (1999). Change of structure in financial time series, long range dependence and the GARCH model. Preprint 99–5–06, IWI, Rijksuniversiteit Groningen.Google Scholar
Perron, P. (1990). Testing for a unit root in a time series with a changing mean. J. Business Econom. Statist. 8, 153162.Google Scholar
Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. J. Econometrics 80, 355385.CrossRefGoogle Scholar
Perron, P., and Vogelsang, T. (1992). Nonstationarity and level shifts with an application to purchasing power parity. J. Business and Econom. Statist., 10, 301320.Google Scholar
Politis, D. N., Romano, J. P., and Wolf, M. (1999). Subsampling. Springer, Berlin.CrossRefGoogle Scholar
Robinson, P. M. (1991). Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. J. Econometrics 47, 6784.CrossRefGoogle Scholar
Teverovsky, V., and Taqqu, M. S. (1997). Testing for long-range dependence in the presence of shifting means or a slowly declining trend, using a variance-type estimator. J. Time Ser. Anal., 18, 279304.CrossRefGoogle Scholar
Willinger, W., Taqqu, M. S., Leland, W. E., and Wilson, D.E. (1995). Self-similarity in high-speed packet traffic: analysis and modeling of Ethernet traffic measurements. Statist. Sci., 10, 6785.CrossRefGoogle Scholar