Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as...

We present evidence that global vectorautoregressive (GVAR) models produce significantly more accurate recession forecasts than country-specific time-series models in a Bayesian framework. This result holds for most countries and forecast horizons as well as for several country groups.

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...

In this paper we propose a time-varying parameter VAR model for the housing market in the United States, the United Kingdom, Japan and the Euro Area. For these four economies, we answer the following research questions: (i) How can we evaluate the stance of monetary policy when the policy rate...

We analyze the interaction between monetary policy in the US and the global economy, using a global vector autoregressive model with time-varying parameters and stochastic volatility (TVP-SV-GVAR). We find that a contractionary US monetary policy shock leads to a persistent fall in international...

We estimate a multivariate unobserved components stochastic volatility model to explain the dynamics of a panel of six exchange rates against the US Dollar. The empirical model is based on the assumption that both countries' monetary policy strategies may be well described by Taylor rules with a...