In vector autoregression models, all variations in variables originate solely from structural shocks
In vector autoregression models, forecast error variance decompositions and structural innovations are standardly modeled under the premise that all variations in the system's variables originate from underlying structural shocks.
The retrieved literature consistently confirms that structural vector autoregression (SVAR) models attribute the variations and dynamics of variables in the system to underlying structural shocks and their associated impulse responses.
Alessio Moneta, Gianluca Pallante. Identification of Structural VAR Models Via Independent Component Analysis: A Performance Evaluation Study. 2022. https://doi.org/10.2139/ssrn.4109830
Discusses how SVAR models recover the impact of independent structural shocks on observed series from estimated residuals.
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Yuriy Gorodnichenko. Reduced-Rank Identification of Structural Shocks in Vars. 2004. https://doi.org/10.2139/ssrn.590906
Integrates factor structures into SVAR analysis to identify structural shocks and study monetary policy effects.
Francesco Cordoni, F. Corsi. Identification of Singular and Noisy Structural VAR Models: The Collapsing-Ica Approach. 2022. https://doi.org/10.2139/ssrn.4153616
Proposes identification methods for singular structural VAR models driven by underlying structural shocks.
Patil S, Savadatti PPM. Rethinking Global Macroeconomic Causality: A Structural VAR Model Based on U.S. Evidence. 2025. https://doi.org/10.21203/rs.3.rs-6720708/v1
Utilizes forecast error variance decomposition and structural VAR methodology to analyze how macroeconomic variables respond to underlying economic shocks.
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