We explore whether modelling parameter time variation improves the point, interval and density forecasts of nine major exchange rates vis-a-vis the US dollar over the period 1976-2015. We find that modelling parameter time variation is needed for an accurate calibration of forecast confidence...

We revisit medium- to long-run exchange rate determination, focusing on the role of international investment positions. To do so, we make use of a new econometric framework accounting for conditional long-run homogeneity in heterogeneous dynamic panel data models. In particular, in our model the...

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 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...

We estimate the effects of a negative asymmetric demand shock on the real exchange rate for the euro area vis-à-vis the United States, Canada, and Japan by state-dependent sign-restricted local projection methods. We find a real depreciation when interest rates are not at the ZLB, but also when...

For the timely detection of business-cycle turning points we suggest to use mediumsized linear systems (subset VARs with automated zero restrictions) to forecast the relevant underlying variables, and to derive the probability of the turning point from the forecast density as the probability...

Evidence on the effectiveness of FX interventions is either limited to short horizons or hampered by debatable identification. We address these limitations by identifying a structural vector autoregressive model for the daily frequency with an external instrument. Applying this approach to the...

This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a...

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...

This paper analyzes the time-varying credibility of the Fed's inflation target in an empirical macro model with asymmetric information, where the public has to learn about the actual inflation target from the Fed's interest rate policy. To capture the evolving communication strategy of the Fed,...