State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering
by
Yukai Yang 1,2,*
and Luc Bauwens 3
Yukai Yang 1,2,* and Luc Bauwens 3
1
Department of Statistics, Uppsala University, P.O. Box 513, SE-75120 Uppsala, Sweden
2
Center for Data Analytics, Stockholm School of Economics, SE-11383 Stockholm, Sweden
3
Center for Operations Research and Econometrics, Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium
*
Author to whom correspondence should be addressed.
Econometrics 2018, 6(4), 48; https://doi.org/10.3390/econometrics6040048
Received: 30 July 2018 / Revised: 25 November 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
(This article belongs to the Special Issue Filtering)
We develop novel multivariate state-space models wherein the latent states evolve on the Stiefel manifold and follow a conditional matrix Langevin distribution. The latent states correspond to time-varying reduced rank parameter matrices, like the loadings in dynamic factor models and the parameters of cointegrating relations in vector error-correction models. The corresponding nonlinear filtering algorithms are developed and evaluated by means of simulation experiments.
View Full-Text
Keywords:
state-space models; Stiefel manifold; matrix Langevin distribution; filtering; smoothing; Laplace method; dynamic factor model; cointegration
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Yang, Y.; Bauwens, L. State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering. Econometrics 2018, 6, 48.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Search more from Scilit
