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ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY
Abstract
Various methods for estimating the self-similarity parameter and/or the intensity of long-range dependence in a time series are available. Some are more reliable than others. To discover the ones that work best, we apply the different methods to simulated sequences of fractional Gaussian noise and fractional ARIMA (0, d, 0). We also provide here a theoretical justification for the method of residuals of regression.
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Cited by
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C.-K., Schmidt, G., Yamamoto, Y., Reviewers:, D., Gorenek, B., et al. (2015) · Europace · doi:10.1093/europace/euv015 · Cited by 727
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Metadata
- Authors
- TAQQU, M. S., TEVEROVSKY, V., & WILLINGER, W.
- Venue
- Fractals
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 1995-12
- Volume
- 03
- Issue
- 04
- Pages
- 785-798
- ISSN
- 0218-348X (ISSN-L)
- 1793-6543
- OpenAlex
- W2165773639
- Cited by
- 1,189
- References
- 0
- FWCI
- 30.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY. https://app.cochat.ai/papers/10.1142/s0218348x95000692
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- TAQQU, M. S., TEVEROVSKY, V., & WILLINGER, W. (1995). ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY. Fractals. https://doi.org/10.1142/s0218348x95000692
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