3 research outputs found
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.Comment: Keywords: Identification Through Heteroskedasticity, Bayesian
Hypotheses Assessment, Markov-switching Models, Mixture Models, Regime Chang