Results 111 to 120 of about 264 (161)

Fast Multi-Order Stochastic Subspace Identifi cation

, 2010
Stochastic subspace identification methods are an efficient tool for system identification of mechanical systems in Operational Modal Analysis (OMA), where modal parameters are estimated from measured vibrational data of a structure. System identification is usually done for many successive model orders, as the true system order is unknown and ...
Döhler, Michael, Mevel, Laurent
openaire   +2 more sources

The Mathematical History Behind the Granger–Johansen Representation Theorem

Oxford Bulletin of Economics and Statistics, Volume 88, Issue 4, Page 587-602, August 2026.
ABSTRACT When can a vector time series that is integrated once (i.e., becomes stationary after taking first differences) be described in error correction form? The answer to this is provided by the Granger–Johansen representation theorem. From a mathematical point of view, the theorem can be viewed as essentially a statement concerning the geometry of ...
Johannes M. Schumacher
wiley   +1 more source

Inference on Common Trends in a Cointegrated Nonlinear SVAR

Oxford Bulletin of Economics and Statistics, Volume 88, Issue 4, Page 658-676, August 2026.
ABSTRACT We consider the problem of performing inference on the number of common stochastic trends when data is generated by a cointegrated CKSVAR (a two‐regime, piecewise affine SVAR; Mavroeidis, 2021), using a modified version of the Breitung (2002) multivariate variance ratio test that is robust to the presence of nonlinear cointegration (of a known
James A. Duffy, Xiyu Jiao
wiley   +1 more source

Large‐Dimensional Cointegrated Threshold Factor Models: The Global Term Structure of Interest Rates

Oxford Bulletin of Economics and Statistics, Volume 88, Issue 4, Page 677-689, August 2026.
ABSTRACT In this paper we extend the two‐level factor model to account for cointegration between group‐specific factors in large datasets. We propose two nonlinear specifications: (i) a threshold vector error correction model (VECM) that allows for asymmetric adjustment across regimes; and (ii) a band VECM that captures state‐dependent adjustment which
Daniel Abreu, Paulo M. M. Rodrigues
wiley   +1 more source

Detecting Sparse Cointegration

Oxford Bulletin of Economics and Statistics, Volume 88, Issue 4, Page 726-741, August 2026.
ABSTRACT We propose a two‐step procedure for detecting sparse cointegration in high‐dimensional single‐equation models. First, we employ the adaptive lasso to identify the subset of integrated covariates driving the long‐run equilibrium relationship.
Jesús Gonzalo, Jean‐Yves Pitarakis
wiley   +1 more source

Finite Sample Analysis of Subspace Identification for Stochastic Systems

14 pages, 2 ...
Sun, Shuai, Hu, Weikang, Wang, Xu
openaire   +2 more sources

On Improving the Efficiency of Bayesian Stochastic Subspace Identification

O’Connell, B.J., Rogers, T.J.
openaire   +2 more sources

Identification of stochastic systems : Subspace methods and covariance extension

, 2001
NR ...
openaire   +1 more source

Robust probabilistic canonical correlations for stochastic subspace identification

, 2022
O'Connell, B.J., Cross, E.J., Rogers, T.J.   +2 more
openaire   +1 more source

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An improved stochastic subspace identification for operational modal analysis

Measurement: Journal of the International Measurement Confederation, 2012
Abstract An improved stochastic subspace identification algorithm is introduced to solve the low computational efficiency problem of the Data-driven stochastic subspace identification. Compared with the conventional algorithm, it needs much less cost of memory and computing time because it does not have a process of the QR decomposition of Hankel ...
Baoping Tang, Guowen Zhang
exaly   +2 more sources