Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks
A structural Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the reduced form error covariance matrices with constraints on the time-varying impact matrix. This leads to flexible identification conditions, and some of the constraints are also testable. The empirical application studies asymmetries in the effects of the U.S. monetary policy shock and finds strong asymmetries with respect to the sign and size of the shock and to the initial state of the economy. The accompanying CRAN distributed R package gmvarkit provides a comprehensive set of tools for numerical analysis.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Gaussian and Student's $t$ mixture vector autoregressive model with application to the effects of the Euro area monetary policy shock
A new mixture vector autoregressive model based on Gaussian and Student's $t$ distributions is introduced. As its mixture components, our model incorporates conditionally homoskedastic linear Gaussian vector autoregressi…
Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models
Linear structural vector autoregressive models can be identified statistically without imposing restrictions on the model if the shocks are mutually independent and at most one of them is Gaussian. We show that this resu…
Causal discovery with scale-mixture model for spatiotemporal variance dependencies
In conventional causal discovery, structural equation models (SEM) are directly applied to the observed variables, meaning that the causal effect can be represented as a function of the direct causes themselves. However,…
Causal DiscoveryNonlinear Forecast Error Variance Decompositions with Hermite Polynomials
A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite…
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation
In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors hav…
Image RetrievalObject RecognitionSentence