We propose a method to estimate time invariant cyclical dynamic stochastic general equilibrium models using the information provided by a variety of filters. We treat data filtered with alternative procedures as contaminated proxies of the relevant model-based quantities and estimate structural...

Im Zentrum dieser Dissertation steht das Beschreiben und Erklären von Konjunkturdynamiken. Motiviert durch den außerordentlich starken wirtschaftlichen Einbruch in 2008/2009 betont die Arbeit dabei die Wichtigkeit der Nutzung von nichtlinearen Modellansätzen. Die Dissertation kann als Beitrag...

This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging...

This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In the empirical application, we forecast U.S. business cycle turning points with state-level employment data. We find that forecasts...

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a...

We study the cyclical dynamics of consumption in the euro area (EA) and the large EA countries by distinguishing durable from nondurable expenditures. We adopt a theoretical partial equilibrium framework to justify the identification strategy of our empirical model, a time-varying parameter...

In this paper, we propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting...

Estimated DSGE models have become the standard workhorse model for empirically based macroeconomic analysis in recent years. In this paper, we present an estimated DSGE model for Denmark. The model has been estimated using Bayesian methods and a dataset consisting of 23 macroeconomic variables....

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...