We propose a dynamic factor model with time-varying parameters and stochastic volatility to analyze the relationship between global factors and country-specific capital flow dynamics. Studying a global sample of 43 countries from 1994 until 2015, we show that global co-movement of macroeconomic,...

In line with the recent policy discussion on the use of macroprudential measures to respond to crossborder risks arising from capital flows, this paper tries to quantify to what extent macroprudential policies (MPPs) have been able to stabilize capital flows in Central, Eastern and Southeastern...

This paper puts forward a Bayesian version of the global vector autoregressive model (B-GVAR) that accommodates international linkages across countries in a system of vector autoregressions. We compare the predictive performance of B-GVAR models for the one- and four-quarter ahead forecast...

In this paper we analyze the impact of three U.S. structural shocks on, and its transmission 0to, the world economy. For that purpose we use a Bayesian version of the global vector autoregressive (GVAR) model coupled with a prior specification that explicitly treats uncertainty regarding...

We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing...

We analyze the interaction between monetary policy in the US and the global economy proposing a new class of Bayesian global vector autoregressive models that accounts for time-varying parameters and stochastic volatility (TVP-SV-GVAR). We find that a contractionary US monetary policy shock...

In this paper we compare the transmission of a conventional monetary policy shock with that of an unexpected decrease in the term spread, which mirrors quantitative easing. Employing a time-varying vector autoregression with stochastic volatility, our results are two-fold: First, the spread...

To assess the performance of multivariate density forecasts for the world economy based on a Bayesian global vector autoregressive (GVAR) model, we decompose the predictive joint density into its marginals and a copula term that captures the dependence structure among variables and countries....

This paper puts forward a Bayesian version of the global vector autoregressive model (B-GVAR) that accommodates international linkages across countries in a system of vec- tor autoregressions. We compare the predictive performance of B-GVAR models for the one- and four-quarter ahead forecast...

We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing...