Set identification in Bayesian vector autoregression (VARs) is becoming increasingly popular while facing recent criticism about potentially unwanted prior dominance and underrepresented bounds of the identified set. This can lead to biased inference even in large samples. Common estimation...

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully...

We analyze set identification in Bayesian vector autoregressions (VARs). Because set identification can be challenging, we propose to include micro data on heterogeneous entities to sharpen inference. First, we provide conditions when imposing a simple ranking of impulse-responses sharpens...

How much have the dynamics of U.S. time series changed over the last century? Has the evolution of the Federal Reserve as an institution over the 100 years altered the transmission of monetary policy shocks? To tackle these questions, we build a multivariate time series model with time-varying...

Should policymakers and applied macroeconomists worry about the difference between real-time and final data? We tackle this question by using a VAR with time-varying parameters and stochastic volatility to show that the distinction between real-time data and final data matters for the impact of...

We propose a novel identification strategy of imposing sign restrictions directly on the impulse responses of a large set of variables in a Bayesian factor-augmented vector autoregression. We conceptualize and formalize conditions under which every additional sign restriction imposed can be...

How much have the dynamics of U.S. time series and in particular the transmission of innovations to monetary policy instruments changed over the last century? The answers to these questions that this paper gives are "a lot" and "probably less than you think," respectively. We use vector...

This paper studies evolving macroeconomic consequences of adverse credit spread shocks for the US economy over the past century. The key objective is to characterize and quantify how the credit transmission mechanism has changed in shaping the macroeconomy during major macroeconomic episodes. I...

Bayesian inference is common in models with many parameters, such as large VAR models, models with time-varying parameters, or large DSGE models. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters. The choice of these hyperparameters is...