We propose a new Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects alternative exclusion restrictions over time and, as a condition for the search, allows to verify identification through heteroskedasticity...

We propose a Bayesian infinite hidden Markov model to estimate time- varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model,...

Reporting point estimates and error bands for structural vector autoregressions that are only set identified is a very common practice. However, unless the researcher is persuaded on the basis of prior information that some parameter values are more plausible than others, this common practice...

Traditional ways of analyzing the effects of monetary policy shocks via structural vector autoregressions require the use of unrealistic identifying assumptions: they either do not allow for a response of output and prices on impact of the shock, or they exclude contemporaneous values of these...

This paper employs Vector Autoregression (VAR) models to measure the impact of monetary policy shocks on regional output in Indonesia. Having incorporated a possible structural break following the aftermath of the 1997-98 Asian Crisis, the impulse response functions derived from the estimated...

This paper proposes a parsimoniously time varying parameter vector autoregressive model (with exogenous variables, VARX) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are assumed to follow parsimonious random walks, where...