Multivariate Reduced Rank Regression in Non-Gaussian Contexts, Using Copulas
University of Cergy-Pontoise - THEMA
Erick Williams Rengifo
Fordham University - Department of Economics - Center for International Policy Studies (CIPS)
CORE Discussion Paper No. 2004/32
We propose a new procedure to perform Reduced Rank Regression (RRR) in non-Gaussian contexts, based on Multivariate Dispersion Models. Reduced-Rank Multivariate Dispersion Models (RR-MDM) generalise RRR to a very large class of distributions, which include continuous distributions like the normal, Gamma, Inverse Gaussian, and discrete distributions like the Poisson and the binomial. A mul-tivariate distribution is created with the help of the Gaussian copula and estimation is performed using maximum likelihood. We show how this method can be amended to deal with the case of discrete data. We perform Monte Carlo simulations and show that our estimator is more efficient than the traditional Gaussian RRR. In the framework of MDM's we introduce a procedure analogous to canonical correlations, which takes into account the distribution of the data.
Number of Pages in PDF File: 14
Keywords: multivariate dispersion model, multivariate statistical analysis, canonical correlations, principal component analsysis
JEL Classification: C35, C39working papers series
Date posted: March 31, 2007
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