Data Science Institute
Semi-Parametric Factor Models for Non-Stationary Time Series
Our previous approach to fitting dynamic non-stationary factor models to multivariate time series is based on the principal components of the time-varying spectral-density matrix. This approach allows 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 introduce a novel semi-parametric approach in which only part of the model is allowed to be time-varying. More precisely, the small-dimensional latent factors admit a dynamic representation with time-varying parameters while the high-dimensional loadings are time-invariant. The time-varying parameters are approximated by local polynomials and estimated by maximizing the likelihood locally.
We provide asymptotic theory, simulation results and applications to real data.
Friday, 2/8/2019, 11:30 AM, BLOC 113