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Log-Periodogram Regression of Time Series with Long Range Dependence

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Robinson, P. M.·1995·The Annals of Statistics

Abstract

This paper discusses the estimation of multiple time series models which allow elements of the spectral density matrix to tend to infinity or zero at zero frequency and be unrestricted elsewhere. A form of log-periodogram regression estimate of differencing and scale parameters is proposed, which can provide modest efficiency improvements over a previously proposed method (for which no satisfactory theoretical justification seems previously available) and further improvements in a multivariate context when differencing parameters are a priori equal. Assuming Gaussianity and additional conditions which seem mild, asymptotic normality of the parameter estimates is established.

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Metadata

Authors
Robinson, P. M.
Venue
The Annals of Statistics
Published
1995-06-01
Volume
23
Issue
3
Pages
1048-1072
ISSN
0090-5364 (ISSN-L)
OpenAlex
W2063827883
Cited by
1,223
References
1
FWCI
57.31
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Log-Periodogram Regression of Time Series with Long Range Dependence. https://app.cochat.ai/papers/10.1214/aos/1176324636
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Robinson, P. M. (1995). Log-Periodogram Regression of Time Series with Long Range Dependence. The Annals of Statistics. https://doi.org/10.1214/aos/1176324636

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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.