Structural Gaussian mixture vector autoregressive model
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A structural version of the Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the error term covariance matrices with zero and sign constraints. It turns out that this often leads to less restrictive identification conditions than in conventional SVAR models, while some of the constraints are also testable. The accompanying R-package gmvarkit provides easy-to-use tools for estimating the models and applying the introduced methods.
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