This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha's (2019) study...

This paper makes the following original contributions to the literature. (1) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions that can be used for models that are overidentified, just-identified, or...

This paper re-examines the evolution of the US monetary transmission mechanism using an empirical framework that incorporates substantially more information than the standard tri-variate VAR model used in most previous studies. In particular, we employ an extended version of a factor-augmented...

We explore the macroeconomic impact of a compression in the long-term bond yield spread within the context of the Great Recession of 2007-2009 via a Bayesian time-varying parameter structural VAR. We identify a ‘pure' spread shock which, leaving the short-term rate unchanged by construction,...

Traditional approaches to structural vector autoregressions can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions...

We construct a monthly real-time data set consisting of vintages for 1991.1-2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques...