This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to...

The prominent role of monetary policy in the U.S. interwar depression has been conventional wisdom since Friedman and Schwartz [1963]. This paper presents evidence on both the surprise and the systematic components of monetary policy between 1929 and 1933. Doubts surrounding GDP estimates for...

This paper examines whether the presence of parameter instabilities in dynamic stochastic general equilibrium (DSGE) models affects their forecasting performance. We apply this analysis to medium-scale DSGE models with and without financial frictions for the US economy. Over the forecast period...

DSGE (Dynamic stochastic general equilibrium) models are the common workhorse of modern macroeconomic theory. Whereas story-telling and policy analysis were in the forefront of applications since its inception, the forecasting perspective of DSGE models is only recently topical. In this study,...

We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

This study uses a Bayesian VAR to demonstrate that the recent house price boom in Germany can be explained by falling interest rates and that higher interest rates are likely suciffient to stop the increase of German house prices. The latter suggests a potential drawback of the current monetary...

DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to find the global maximum within the relevant parameter...