Abstract Details
Activity Number:
|
285
|
Type:
|
Invited
|
Date/Time:
|
Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Business and Economic Statistics Section
|
Abstract - #307092 |
Title:
|
Locally Stationary Latent Factors
|
Author(s):
|
Giovanni Motta*+ and Michael Eichler
|
Companies:
|
Columbia University and Maastricht University
|
Keywords:
|
Local stationarity ;
Factor Models ;
Semi-parametric ;
Kalman filter ;
Kalman smoother ;
Local polynomials
|
Abstract:
|
Current approaches for fitting dynamic non-stationary factor models to multivariate time series are based on the principal components of the time-varying spectral matrix.
These approaches allow the spectral matrix to be smoothly time-varying, which imposes very little structure on the moments of the underlying process. However, the estimation delivers time-varying filters that are high-dimensional and two-sided. Moreover, the estimation of the spectral matrix strongly depends on the chosen bandwidths for smoothing over frequency and time.
As an alternative, we propose a semi-parametric approach in which only part of the model is allowed to be time-varying. More precisely, the latent factors admit a dynamic representation with time-varying autoregressive coefficients while the loadings are constant over time.
Estimation of the model parameters is accomplished by application of the EM algorithm and the Kalman filter. The time-varying parameters are modeled locally by polynomials and estimated by maximizing the likelihood locally. Compared to estimation of the factors by principal components, our approach produces superior results in particular for small cross-sectional dimension.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.