Abstract
Much progress has been made in recent years in multivariate time-series analysis. In this paper we summarize some of the methodological developments that are particularly relevant to empirical economics and highlight especially the usefulness of linear transformations in analyzing multivariate time series. The topics considered include vector ARMA models, principal component analysis, scalar component models, canonical correlation analyses, co-integration, and unit-root tests. We illustrate the methods considered by an example using Taiwan’s interest-rate series and provide critiques of these developments.
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© 1994 Physica-Verlag Heidelberg
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Tiao, G.C., Tsay, R.S., Wang, T. (1994). Usefulness of Linear Transformations in Multivariate Time-Series Analysis. In: Dufour, JM., Raj, B. (eds) New Developments in Time Series Econometrics. Studies in Empirical Economics. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-48742-2_2
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DOI: https://doi.org/10.1007/978-3-642-48742-2_2
Publisher Name: Physica-Verlag HD
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