A Canonical Analysis of Multiple Time Series.
Interim technical rept.,
WISCONSIN UNIV MADISON DEPT OF STATISTICS
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This paper proposes a canonical transformation of a k dimensional stationary autoregressive process. The components of the transformed process are ordered from least predictable to most predictable. The least predictable components are often nearly white noise and the most predictable can be nearly nonstationary. Transformed variables which are white noise can reflect relationships which may be associated with or point to economic or physical laws. A 5-variate example is given.
- Theoretical Mathematics