The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Fitting time series models to nonstationary processes
Open accessAbstract
A general minimum distance estimation procedure is presented for nonstationary time series models that have an evolutionary spectral representation. The asymptotic properties of the estimate are derived under the assumption of possible model misspecification. For autoregressive processes with time varying coefficients, the estimate is compared to the least squares estimate. Furthermore, the behavior of estimates is explained when a stationary model is fitted to a nonstationary process.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Time Series: Theory and Methods
Ziegel, E. R., Brockwell, P., & Davis, R. (1992) · Technometrics · doi:10.2307/1270070 · Cited by 5,012
Spectral Analysis and Time Series
Preston, D. B. (1983) · Technometrics · doi:10.1080/00401706.1983.10487866 · Cited by 5,011
Time Series: Data Analysis and Theory.
Koopmans, L. H., & Brillinger, D. R. (1976) · Journal of the American Statistical Association · doi:10.2307/2285347 · Cited by 2,637
Hannan, E. J. (1970) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316429 · Cited by 2,369
Evolutionary Spectra and Non-Stationary Processes
Priestley, M. B. (1965) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1965.tb01488.x · Cited by 1,080
Time Series: Theory and Methods
Brockwell, P. J., & Davis, R. A. (1987) · Springer Series in Statistics · doi:10.1007/978-1-4899-0004-3 · Cited by 1,072
Non-linear and non-stationary time series analysis
Priestley, M. (1988) · Medical Entomology and Zoology · Cited by 1,045
Time Series: Data Analysis and Theory.
Brillinger, D. R. (1981) · Biometrics · doi:10.2307/2530198 · Cited by 799
Estimation and information in stationary time series
Whittle, P. (1953) · Arkiv för Matematik · doi:10.1007/bf02590998 · Cited by 558
Cited by
Wavelet Threshold Estimators for Data with Correlated Noise
Johnstone, I. M., & Silverman, B. W. (1997) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00071 · Cited by 833
A review on prognostic techniques for non-stationary and non-linear rotating systems
Kan, M. S., Tan, A. C. C., & Mathew, J. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.02.016 · Cited by 381
Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis
Miwakeichi, F., Martı́nez-Montes, E., Valdés-Sosa, P. A., Nishiyama, N., Mizuhara, H., & Yamaguchi, Y. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.039 · Cited by 374
Modelling Nonlinear Economic Time Series
Teräsvirta, T., Tjøstheim, D., & Granger, C. W. J. (2010) · doi:10.1093/acprof:oso/9780199587148.001.0001 · Cited by 352
The Impact of Bootstrap Methods on Time Series Analysis
Politis, D. N. (2003) · Statistical Science · doi:10.1214/ss/1063994977 · Cited by 345
Metadata
- Authors
- Dahlhaus, R.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1997-02-01
- Volume
- 25
- Issue
- 1
- Pages
- 1-37
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W1990139510
- Cited by
- 924
- References
- 19
- FWCI
- 23.48
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
