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...

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...

Recently, Baumeister and Hamilton (henceforth: BH) have argued that existing studies of the global oil market fail to account for uncertainty about their identifying assumptions. They recommend an alternative econometric approach intended to address this concern by formulating priors on the...

We use a time-varying parameter dynamic factor model with stochastic volatility (DFM-TV-SV) estimated using Bayesian methods to disentangle the relative importance of the common component in FHFA house price movements from state-specific shocks, over the quarterly period of 1975Q2 to 2017Q4. We...

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 analyse the macroeconomic effects of exogenous contractions in bank lending to non-financial corporations in the Euro Area, Germany, France, Italy and Spain using a Bayesian vector autoregressive model with endogenous hyperparameter selection and identification via sign restrictions. We focus...

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...

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a...

This paper analyses the effects of loan supply, as well as aggregate demand, aggregate supply and monetary policy shocks between 1998 and 2014 in Macedonia using a structural Vector Auto Regression with sign restrictions and Bayesian estimation. The main results indicate that loan supply shocks...

Structural VAR models are frequently identified using sign restrictions on contemporaneous impulse responses. We develop a methodology that can handle a set of prior distributions that is much larger than the one currently allowed for by traditional methods. We then develop an importance sampler...