Policy counterfactuals based on estimated structural VARs routinely suggest that bringing Alan Greenspan back in the 1970s' United States would not have prevented the Great Inflation. We show that a standard policy counterfactual suggests that the Bundesbank – which is near-universally...

We study the identification of policy shocks in Bayesian proxy VARs for the case that the instrument consists of sparse qualitative observations indicating the signs of certain shocks. We propose two identification schemes, i.e. linear discriminant analysis and a non-parametric sign concordance...

We use a Bayesian time-varying parameters structural VAR with stochastic volatility for GDP deflator inflation, real GDP growth, a 3-month nominal rate, and the rate of growth of M4 to investigate the underlying causes of the Great Moderation in the United Kingdom. Our evidence points towards a...

The paper provides a novel Bayesian methodological framework to estimate structural VAR (SVAR) models with recursive identification schemes that allows for the inclusion of over-identifying restrictions. The proposed framework enables the researcher to elicit the prior on the non-zero...

We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for...

We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for...

We propose a class of prior distributions that discipline the long-run behavior of Vector Autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run...

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three \Vs": the large number of time series continuously...

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...