Robust Analysis of the Causality in Subset ARX Models
Version 2.1.1 (10,1 ko) par
Carlo Grillenzoni
Analysis of Causality between time series Xt, Yt with ARX models which have an irregular (subset) structure, by means of Robust estimators
These codes perform analysis of the Granger causality between two time series Xt,Yt through subset ARX(p,q) models which have an irregular structure. Namely, they have sparse coefficients within maximum order lags p,q. Model identification is carried out with backward stepwise OLS regression with heteoskedastic consistent (HC) standard errors. The indicators of causality are F-statistics (on reduction of the residual variance) and Gain parameters (with T-statistics). Recently a Robust M-estimator version is also provided with demos for additive outliers and GARCH residuals.
Citation pour cette source
Carlo Grillenzoni (2024). Robust Analysis of the Causality in Subset ARX Models (https://www.mathworks.com/matlabcentral/fileexchange/99979-robust-analysis-of-the-causality-in-subset-arx-models), MATLAB Central File Exchange. Récupéré le .
Compatibilité avec les versions de MATLAB
Créé avec
R2021b
Compatible avec toutes les versions
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Version | Publié le | Notes de version | |
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2.1.1 | Version 2.1 |
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2.1.0 | Version 2.1 |
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2.0.0 | Robust M-estimation and new demos are provided |
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1.0.1 | Commentary change |
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1.0.0.1 | Change figure |
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1.0.0 |