Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings...

The analysis of large panel data sets (with N variables) involves methods of dimension reduction and optimal information extraction. Dimension reduction is usually achieved by extracting the common variation in the data into few factors (k, where k N). In the present project, factors are...

We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings...

The number of employees historically filed and registered from January to April 2020 for short-time compensation is used to obtain a nowcast for GDP growth in the first quarter and an outlook until the third quarter 2021. We purge the monthly log level series from the systematic component to...

The purpose of this paper is to model both loans to households and to non-financial corporations as well as their relation to interest rates and demand variables for Austria, Germany, the Netherlands and the United Kingdom. Credit aggregates are modeled using a Markov-switching vector...

This paper analyzes the recently documented instability of money demand in the euro area in the framework of a Markov switching trend model. First, we consider a standard flexible price model with stable money demand, rational expectations, and an exogenous income-money ratio which follows a...

We document whether a simple, univariate model for quarterly GDP growth is able to deliver forecasts in a crisis period like the Covid-19 pandemic, which may serve cross-checking forecasts obtained from elaborate and expert models used by forecasting institutions. We include shocks to the log...

We document whether a simple, univariate model for quarterly GDP growth is able to deliver forecasts of yearly GDP growth in a crisis period like the Covid19 pandemic, which may serve cross-checking forecasts obtained from elaborate and expert models used by forecasting institutions. We include...