Abstract #301687

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JSM 2003 Abstract #301687
Activity Number: 274
Type: Invited
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301687
Title: Semiparametric Reduced-rank Regression
Author(s): Kung-Sik Chan*+ and Ming-Chung Li
Companies: University of Iowa and EMMES Corporation
Address: Dept. of Stat. & Actuarial Science, Iowa City, IA, 52242,
Keywords: common structure ; cross-validation ; local polynomial regression ; nonlinear co-integration ; panel of time series ; strongly mixing
Abstract:

We propose a new semiparametric approach for inferring the structure of the regression function of a multivariate response Y on a multivariate covariate X. Specifically, we propose a new dimension-reduction approach, namely, the Semiparametric Reduced-rank Regression (SPARR) model, by adapting the reduced-rank linear regression technique. The SPARR model specifies that the conditional mean of Y depends linearly on some nonlinear principal components, which depend on some indices of X. The SPARR model provides a framework for classifying the common regression structure among the components of Y and/or studying nonlinear co-integration relationships for multivariate time series. We discuss an estimation scheme for the SPARR model and the large-sample properties of the estimator for both the parametric and the nonparametric part of the model. The new approach is illustrated with real data.

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