Abstract
The classical multivariate regression model presented in Chapter 1, as noted before, does not make direct use of the fact that the response variables are likely to be correlated. A more serious practical concern is that even for a moderate number of variables whose interrelationships are to be investigated, the number of parameters in the regression matrix can be large. For example, in a multivariate analysis of economic variables (see Example 2.2), Gudmundsson (1977) uses m = 7 response variables and n = 6 predictor variables, thus totaling 42 regression coefficient parameters (excluding intercepts) to be estimated, in the classical regression setup. But the number of vector data points available for estimation is only T = 36; these are quarterly observations from 1948 to 1956 for the United Kingdom. Thus, in many practical situations, there is a need to reduce the number of parameters in model (1.1) and we approach this problem through the assumption of lower rank of the matrix C in model (1.1). More formally, in the model Y k = CX k + εk we assume that
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© 1998 Springer Science+Business Media New York
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Reinsel, G.C., Velu, R.P. (1998). Reduced-Rank Regression Model. In: Multivariate Reduced-Rank Regression. Lecture Notes in Statistics, vol 136. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2853-8_2
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DOI: https://doi.org/10.1007/978-1-4757-2853-8_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98601-2
Online ISBN: 978-1-4757-2853-8
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