A growing literature stresses the importance of the “global financial cycle”, a common global movement in asset prices and credit conditions, for emerging market economies (EMEs). It is argued that one of the key drivers of this global cycle is monetary policy in the U.S., which is...

We study cross-country differences in monetary policy transmission across the large four euro-area countries (France, Germany, Italy and Spain) using a large Bayesian vector autoregressive model with endogenous prior selection. Drawing both on the posterior distributions of the cross-country...

Applying a BVAR model, the present paper first identifies the possible drivers of Germany's TARGET claims. In this context, in terms of potential causes, a distinction is made between a rise in the global risk assessment, tensions within the euro area, and European monetary policy. It becomes...

Most analyses of the U.S. Great Moderation have been based on VAR methods, and have consistently pointed toward good luck as the main explanation for the greater macroeconomic stability of recent years. Using data generated by a New-Keynesian model in which the only source of change is the move...

This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the seemingly unrelated regression and vector autoregressive models are...

The European Central Bank's asset purchase programs, while intended to stabilize the economy, may have unintended side effects on financial stability. This paper aims at gauging the effects on financial markets, the banking sector, and lending to non-financial firms. Using a structural vector...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying....

The severity function approach (abbreviated SFA) is a method of selecting adverse scenarios from a multivariate density. It requires the scenario user (e.g. an agency that runs banking sector stress tests) to specify a "severity function", which maps candidate scenarios into a scalar severity...

To simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural changes, we introduce a time-varying parameter mixed-frequency VAR. To keep our approach from becoming too complex, we implement time variation parsimoniously: only the intercepts and a common...

The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting...