The Co-Integrated Vector Autoregression with Errors–in–Variables
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Publication:5864352
DOI10.1080/07474938.2013.806853zbMath1491.62120OpenAlexW1991839972MaRDI QIDQ5864352
Publication date: 7 June 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/07474938.2013.806853
Kalman filterstate-space modelmeasurement errorsexpectation-maximization (EM) algorithmyield curve dynamicsco-integrated vector autoregression
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
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- AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
- From general state-space to VARMAX models
- Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models
- An improved Akaike information criterion for state-space model selection
- On the convergence properties of the EM algorithm
- Prediction of multivariate time series by autoregressive model fitting
- Statistical analysis of cointegration vectors
- O a lemma associated with Box, Jenkins and Granger
- On the structure of moving average processes
- Testing cointegration in infinite order vector autoregressive processes
- Comparison of tests for the cointegrating rank of a VAR process with a structural shift
- On the resultant property of the Fisher information matrix of a vector ARMA process
- Maximum Likelihood Estimation from Incomplete Data
- Errors in Variables and Cointegration
- Cointegration analysis under measurement errors
- Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
- Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models
- Statistical algorithms for models in state space using SsfPack 2.2
- Estimation and Testing for Unit Roots in a Partially Nonstationary Vector Autoregressive Moving Average Model
- Identification in Parametric Models
- Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models
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