Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes. The switching between the two VAR processes is governed by a two state Markov chain with transition probabilities that depend on how long...

Many recent papers in macroeconomics have used large Vector Autoregressions (VARs) involving a hundred or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital in achieving reasonable results. Computational concerns currently limit the range of priors...

We provide new insights into determinants of international interest rates spillovers across seven advanced economies. To disentangle and quantify their respective importance, we identify country-specific structural monetary policy, demand, and supply equations in a Bayesian structural panel...

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as...

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a...

There has been a call for caution when using the conventional method for Bayesian inference in setidentified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for individual impulse responses or other quantity of...

In this paper, we assess whether key relations between US interest rates have been stable over time. This is done by estimating trivariate hybrid time-varying parameter Bayesian VAR models with stochastic volatility for the three-month Treasury bill rate, the slope of the Treasury yield curve...

We show that under standard assumptions the elements of the impact matrix of the structural vector autoregression (SVAR) are always at least set identified and bounded by the standard deviations of the corresponding reduced form errors. This result facilitates valid Bayesian inference without...

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 exclusion restrictions. Three contributions emerge from our exercise: (i) a set...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....