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Generalized canonical regression

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Arturo Estrella

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Abstract

This paper introduces a generalized approach to canonical regression, in which a set of jointly dependent variables enters the left-hand side of the equation as a linear combination, formally like the linear combination of regressors in the right-hand side of the equation. Natural applications occur when the dependent variable is the sum of components that may optimally receive unequal weights or in time series models in which the appropriate timing of the dependent variable is not known a priori. The paper derives a quasi-maximum likelihood estimator as well as its asymptotic distribution and provides illustrative applications.

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Publisher Info
Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 288.

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Date of creation: 2007
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Handle: RePEc:fip:fednsr:288

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Related research
Keywords: Time-series analysis ; Econometric models;

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References listed on IDEAS
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  1. Frederic S. Mishkin, 1990. "What Does the Term Structure Tell Us About Future Inflation?," NBER Working Papers 2626, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Arturo Estrella, 2005. "Why Does the Yield Curve Predict Output and Inflation?," Economic Journal, Royal Economic Society, vol. 115(505), pages 722-744, 07. [Downloadable!] (restricted)
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This page was last updated on 2009-11-18.

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