The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Model Specification in Multivariate Time Series
Abstract
SUMMARY: We propose a method to specify an appropriate yet parsimonious vector autoregressive moving average (ARMA) model for a given multivariate time series. By considering contemporaneous linear transformations of the vector process, we introduce the concept of scalar component models within the vector ARMA framework (a) to reveal possibly hidden simplifying structures of the process, (b) to achieve parsimony in parameterization and (c) to identify the exchangeable models. The simplifying structures are of particular importance in the analysis of multivariate time series because they are often not obvious from the observed data but can be used to gain insights to the process under study. The analytical tool used to search for scalar component models is a canonical correlation analysis of vector processes and the proposed procedures are illustrated via two real examples.
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Cites
A new look at the statistical model identification
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Co-integration and error correction: representation, estimation and testing
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Time Series Analysis: Forecasting and Control
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An Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business & Economic Statistics · doi:10.2307/1391399 · Cited by 9,082
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RELATIONS BETWEEN TWO SETS OF VARIATES
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Forecasting, Structural Time Series Models and the Kalman Filter
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Cited by
Analysis of Financial Time Series
(2006) · Technometrics · doi:10.1198/tech.2006.s405 · Cited by 2,348
Elements of Artificial Neural Networks
Mehrotra, K., Mohan, C., & Ranka, S. (1996) · The MIT Press eBooks · doi:10.7551/mitpress/2687.001.0001 · Cited by 912
Forecasting the behavior of multivariate time series using neural networks
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Metadata
- Authors
- Tiao, G. C., & Tsay, R. S.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1989-01-01
- Volume
- 51
- Issue
- 2
- Pages
- 157-195
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W206177690
- Cited by
- 354
- References
- 38
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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