Identification of canonical models for vectors of time series: a subspace approach
From MaRDI portal
Publication:6579386
DOI10.1007/S00362-023-01451-YMaRDI QIDQ6579386
J. Casals, Miguel Jerez, Alfredo García-Hiernaux
Publication date: 25 July 2024
Published in: Statistical Papers (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Vector autoregressive moving average identification for macroeconomic modeling: a new methodology
- From general state-space to VARMAX models
- Statistical analysis of novel subspace identification methods
- Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
- Identification of echelon canonical forms for vector linear processes using least squares
- Estimating the dimension of a model
- Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
- 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
- A fast and stable method to compute the likelihood of time invariant state-space models.
- Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms
- Asymptotic properties of subspace estimators
- Estimating cointegrated systems using subspace algorithms
- A unifying theorem for three subspace system identification algorithms
- Consistency and relative efficiency of subspace methods
- Analysis of the asymptotic properties of the MOESP type of subspace algorithms
- Estimating the system order by subspace methods
- IDENTIFYING MULTIVARIATE TIME SERIES MODELS
- Multivariate linear time series models
- Balanced Approximation of Stochastic Systems
- Fast estimation methods for time-series models in state–space form
- A method for autoregressive-moving average estimation
- ARMA models, their Kronecker indices and their McMillan degree
- Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root
- Modeling Multiple Times Series with Applications
- Recursive estimation of mixed autoregressive-moving average order
- SOME ASPECTS OF MODELLING AND FORECASTING MULTIVARIATE TIME SERIES
- On the identification of ARMA echelon-form models
- On a measure of lack of fit in time series models
- A Unified Approach to Identifying Multivariate Time Series Models
- An Exact Multivariate Model-Based Structural Decomposition
- Balanced canonical forms for system identification
- Two Canonical VARMA Forms: Scalar Component Models Vis-à-Vis the Echelon Form
- Single and multiple error state-space models for signal extraction
- Unit roots and cointegration modelling through a family of flexible information criteria
- Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs
- Forecasting linear dynamical systems using subspace methods
- ESTIMATING LINEAR DYNAMICAL SYSTEMS USING SUBSPACE METHODS
- Elements of multivariate time series analysis.
- Order estimation for subspace methods
- Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models
- A new look at the statistical model identification
This page was built for publication: Identification of canonical models for vectors of time series: a subspace approach
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6579386)