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Clifford Lam, LSE Print
Thursday, 26 April 2012, 12:15 - 13:15

Clifford Lam, London School of Economics


Factor Modeling for High-Dimentional Time Series: Inference for the Number of Factors

Abstract: In this talk, we investigate factor modelling when the number of time series grows with sample size. In particular we focus on the case when the number of time series is at least has the same order as the sample size. We introduce a method utilizing the autocorrelations of time series for estimation of the factor loading matrix and the factors series, which in the end is equivalent to an eigenanalysis of a non-negative definite matrix.

Asymptotic properties will be presented, as well as the choice of the number of factors by an eye-ball test. Theories about such an eye-ball test will also be presented. The method will be illustrated with an analysis of a set of mean sea level pressure (MSLP) data, as well as extensive simulation results. Some new results about standard PCA will also be presented, which shows that PCA still works when the noise vector in the factor model is cross-sectionally correlated to a certain extent, beyond which consistency of factor loading matrix is not guaranteed. The method we introduce, however, do not suffer from heavy cross-sectional correlations in the noise.

An improvement of our method will also be introduced when we have time, showing that it is possible to achieve the superb performance of PCA under classical settings, and at the same time performs better than PCA when noise level and cross-sectional correlations becomes strong.

Location: R42.2.113
Contact: Claude Adan, This e-mail address is being protected from spam bots, you need JavaScript enabled to view it