We study how financial factors shape and interact with the U.S. business cycle through a unified empirical approach where we jointly estimate financial and business cycles as well as identify their underlying drivers using a medium-scale Bayesian Vector Autoregression. First, we show, both in...

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...

Detrending within structural vector autoregressions (SVAR) is directly linked to the shock identification. We investigate the consequences of trend misspecification in an SVAR using both standard real business cycle models and bi-variate SVARs as data generating processes. Our bias decomposition...

We jointly estimate the U.S. business and financial cycle through a unified empirical approach while simultaneously accounting for the role of financial factors. Our approach uses the Beveridge-Nelson decomposition within a medium-scale Bayesian Vector Autore-gression. First, we show, both in...

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...