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...

This paper estimates a switching autoregressive conditional heteroskedastic time series model for returns on the daily German stock market index. Volatility clustering is captured by persistent periods of different volatility levels and by the dependence on past innovations. We introduce a...

If firms borrow working capital to finance production, then nominal interest rates have a direct influence on inflation dynamics, which appears to be the case empirically. However, interest rates may only partly mirror the cost of working capital. In this paper we explore the role of bank...

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 analyze quarterly occupation-level data from the US Current Population Survey for 1976-2013. Based on common cyclical employment dynamics, we identify two clusters of occupations that roughly correspond to the widely discussed notion of "routine" and "non-routine" jobs. After decomposing the...

Two Bayesian sampling schemes are outlined to estimate a K-state Markov switching model with time-varying transition probabilities. Data augmentation for the multinomial logit model of the transition probabilities is alternatively based on a random utility and a difference in random utility...

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 short-time workers from January to April 2020 is used to now- and forecast quarterly GDP growth. We purge the monthly log level series from the systematic component to extract unexpected changes or shocks to log short-time workers. These monthly shocks are included in a univariate...