The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic...

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving...

We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A...

This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a "large" panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar...

The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic...

The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is generally accepted that forecast aggregation is...

Reliable and timely information about current economic conditions is crucial for policy makers and expectations formation. This paper demonstrates the efficacy of the Survey of Professional Forecasters (SPF) and the Purchasing Manager Indices (PMI) in anticipating US real economic activity. We...

In this paper we extract latent factors from a large cross-section of commodity prices, including fuel and non-fuel commodities. We decompose each commodity price series into a global (or common) component, block-specific components and a purely idiosyncratic shock. We find that the bulk of the...