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 develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are...

In this paper, I examine the forecasting performance of a Bayesian Vector Autoregression (BVAR) model with steady-state prior and compare the accuracy of the forecasts against the forecasts of QPM model and official NBU forecasts over the period 2016q1-2020q1. My findings suggest that inflation...

Imports represent a relevant component of total economic resources. For the Italian case, they mainly consist of raw materials and intermediate goods. In this paper, we evaluate several econometric models performing shorthorizon forecasts of Italian imports of goods. Year-to-year growth rate of...

I present evidence that the linear mixed-frequency Bayesian VAR provides very sharp and well calibrated monthly real-time recession probabilities for the euro area for the period from 2004 until 2013. The model outperforms not only the univariate regime-switching models for a number of hard and...

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates...

The present paper presents three different short-term oil models on a weekly basis. With these models we try to forecast oil prices out-of-sample up to three months. Two of the models are based on the VAR methodology and consider fundamental factors like the net long position and oil...

In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naive benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on...

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.

This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables, estimated shrinkage, and no nonlinearity. Then I...