We demonstrate how Bayesian shrinkage can address problems with utilizing large information sets to calculate trend and cycle via a multivariate Beveridge-Nelson (BN) decomposition. We illustrate our approach by estimating the U.S. output gap with large Bayesian vector autoregressions that...

We consider how to estimate the trend and cycle of a time series, such as real GDP, given a large information set. Our approach makes use of the Beveridge-Nelson decomposition based on a vector autoregression, but with two practical considerations. First, we show how to determine which...

We develop uncertainty indices for the United States and Australia based on freely accessible, real time Google Trends data. Our Google Trends Uncertainty (GTU) indices are found to be positively correlated to a variety of alternative proxies for uncertainty available for these two countries....