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 which labor market variables are the most informative for estimating and now-casting the U.S. output gap using a multivariate trend-cycle decomposition. Although the unemployment rate clearly contains important cyclical information, it also appears to reflect more persistent...

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 propose a way to directly nowcast the output gap using the Beveridge-Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of...

We propose a way to directly nowcast the output gap using the Beveridge-Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of...