The sample autocorrelation function and the detection of long-memory processes
The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to be identified using the sum of the sample autocorrelations, as usually defined. The reason for this is that the sample sum is a predetermined constant for any stationary time series; a result that is independent of the sample size. Diagnostic or estimation procedures, such as those in the frequency domain, that embed this sum are equally open to this criticism. We develop this result in the context of long memory, extending it to the implications for the spectral density function and the variance of partial sums of a stationary stochastic process. The results are further extended to higher order sample autocorrelations and the bispectral density. The corresponding result is that the sum of the third order sample (auto) bicorrelations at lags h,k≥1, is also a predetermined constant, different from that in the second order case, for any stationary time series of arbitrary length.
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Volume (Year): 391 (2012)
Issue (Month): 24 ()
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- Lobato, Ignacio N & Savin, N E, 1998.
"Real and Spurious Long-Memory Properties of Stock-Market Data,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 16(3), pages 261-68, July.
- I.N. Lobato & N.E. Savin, 1996. "Real and Spurious Long Memory Properties of Stock Market Data," Econometrics 9605004, EconWPA, revised 26 Sep 1996.
- Lobato, I.N. & Savin, N.E., 1996. "Real and Spurious Long Memory Properties of Stock Market Data," Working Papers 96-07, University of Iowa, Department of Economics.
- Serinaldi, Francesco, 2010. "Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2770-2781.
- Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
- Hassani, Hossein, 2010. "A note on the sum of the sample autocorrelation function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1601-1606.
- Lo, Andrew W. & MacKinlay, A. Craig, 1989.
"The size and power of the variance ratio test in finite samples : A Monte Carlo investigation,"
Journal of Econometrics,
Elsevier, vol. 40(2), pages 203-238, February.
- Andrew W. Lo & Craig A. MacKinlay, . "The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation," Rodney L. White Center for Financial Research Working Papers 28-87, Wharton School Rodney L. White Center for Financial Research.
- Andrew W. Lo & A. Craig MacKinlay, 1988. "The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation," NBER Technical Working Papers 0066, National Bureau of Economic Research, Inc.
- Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October.
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