Yue Yu : Dynamic Cluster-based Sliced Inverse Regression for Forecasting Microeconomics Variables

Posted by , part of the Statistics and Data Science Seminar.

At

Oct. 6, 2010, 3 p.m.

In

SEO 636

Abstract

Sliced Inverse Regression (SIR) proposed by Ker-Chau Li (1991) is a widely used semiparametric technique to reduce the dimensions of regression problems. But the microeconomics data are time dependent and usually highly correlated. In our study, we use clustering methods along with SIR, in order to reduce the multicollinearity and the difficulty of choosing the dimensions. And the dynamic version of cluster SIR methods is used to analyze the autoregressive model of the microeconomics data. Our simulation result shows that the dynamic cluster SIR is superior, comparing with the empirical accuracy of all the models in Stock and Watson's paper (2005) for forecasting U.S. macroeconomic time series over a 30-year period.