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Generalized canonical regression

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  • Arturo Estrella

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.

Suggested Citation

  • Arturo Estrella, 2007. "Generalized canonical regression," Staff Reports 288, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:288
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    References listed on IDEAS

    as
    1. Mishkin, Frederic S., 1990. "What does the term structure tell us about future inflation?," Journal of Monetary Economics, Elsevier, vol. 25(1), pages 77-95, January.
    2. Harry L. Margulis, 1998. "Predicting the Growth and Filtering of At-risk Housing: Structure Ageing, Poverty and Redlining," Urban Studies, Urban Studies Journal Limited, vol. 35(8), pages 1231-1259, July.
    3. Arturo Estrella, 2005. "Why Does the Yield Curve Predict Output and Inflation?," Economic Journal, Royal Economic Society, vol. 115(505), pages 722-744, July.
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    Keywords

    time series analysis; Econometric models;

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