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Canonical Correlation in Multivariate Time Series Analysis with an Application to One-Year-Ahead and Multiyear-Ahead Macroeconomic Forecasting

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  • Otter, Pieter W
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    Abstract

    A simple one-period-ahead and multiperiod-ahead prediction procedure for multivariate time series is suggested, based on the canonical correlation technique. The prediction procedure is direct in the sense that no lag orders and parameters have to be estimated first, as in the usual ARMAX or VAR parameterizations of multivariate stationary stochastic processes. A best (in the mean squared error sense) predictor can be obtained directly using singular-value decompositions of covariance matrices. The procedure is used to forecast one-year-ahead and multiyear-ahead national growth rates of 14 countries for the years 1974-84.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

    Volume (Year): 8 (1990)
    Issue (Month): 4 (October)
    Pages: 453-57

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    Handle: RePEc:bes:jnlbes:v:8:y:1990:i:4:p:453-57

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    Cited by:
    1. Wai-Sum Chan, 1999. "Exact joint forecast regions for vector autoregressive models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(1), pages 35-44.
    2. Majid Al-Sadoon, 2013. "Geometric and Long Run Aspects of Granger Causality," Working Papers 682, Barcelona Graduate School of Economics.
    3. Al-Sadoon, M.M., 2009. "Causality Along Subspaces: Theory," Cambridge Working Papers in Economics 0919, Faculty of Economics, University of Cambridge.

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