Structural VAR models are routinely estimated by Bayesian methods. Several recent studies have voiced concerns about the common use of posterior median (or mean) response functions in applied VAR analysis. In this paper, we show that these response functions can be misleading because in...

The authors show that in weakly identified models (1) the posterior mode will not be a consistent estimator of the true parameter vector, (2) the posterior distribution will not be Gaussian even asymptotically, and (3) Bayesian credible sets and frequentist confidence sets will not coincide...

We assess the stability of the unemployment gap parameter using linear dynamic Phillips curve models for the United States. In this study, we allow the unemployment gap parameter to be time-varying such that we can monitor the importance of the Phillips curve over time. We consider different...

In a series of recent studies, Raffaella Giacomini and Toru Kitagawa have developed an innovative new methodological approach to estimating sign-identified structural VAR models that seeks to build a bridge between Bayesian and frequentist approaches in the literature. Their latest paper with...

Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact in the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between...

Baumeister and Hamilton (2019a) assert that every critique of their work on oil markets by Kilian and Zhou (2019a) is without merit. In addition, they make the case that key aspects of the economic and econometric analysis in the widely used oil market model of Kilian and Murphy (2014) and its...

A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different...