Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity
In this study, Bayesian inference is developed for structural vector autoregressive models in which the structural parameters are identified via Markov-switching heteroskedasticity. In such a model, restrictions that are just-identifying in the homoskedastic case, become over-identifying and can be tested. A set of parametric restrictions is derived under which the structural matrix is globally or partially identified and a Savage-Dickey density ratio is used to assess the validity of the identification conditions. The latter is facilitated by analytical derivations that make the computations fast and numerical standard errors small. As an empirical example, monetary models are compared using heteroskedasticity as an additional device for identification. The empirical results support models with money in the interest rate reaction function.
Code (1)
Tasks
Bayesian InferenceSimilar Papers 제목 키워드 기반
Bayesian Analyses of Structural Vector Autoregressions with Sign, Zero, and Narrative Restrictions Using the R Package bsvarSIGNs
The R package bsvarSIGNs implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. It offers fast and efficient estimation…
A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs
We develop a new algorithm for inference based on structural vector autoregressions (SVARs) identified with sign restrictions. The key insight of our algorithm is to break apart from the accept-reject tradition associate…
Bayesian InferencePartial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference
We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exc…
Bayesian InferenceProjection Inference for set-identified SVARs
We study the properties of projection inference for set-identified Structural Vector Autoregressions. A nominal $1-\alpha$ projection region collects the structural parameters that are compatible with a $1-\alpha$ Wald e…
Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars
The R package bsvars provides a wide range of tools for empirical macroeconomic and financial analyses using Bayesian Structural Vector Autoregressions. It uses frontier econometric techniques and C++ code to ensure fast…
C++ code