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. The multinomial logit model for the transition probabilities is alternatively expressed as a random utility model and as a difference random utility model. 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...

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly informative for the factors. To identify the irrelevant variables, we search for zero rows in the loadings matrix of the factor model. To sharply separate these irrelevant...

This paper investigates both cross-sectional asymmetry (related to bank-specific characteristics like size and liquidity) and asymmetries over time (potentially related to the overall state of the economy) in Austrian bank lending reaction to monetary policy. The first type of asymmetry is...

We analyze the interaction between credit and asset prices in the transmission of shocks to the real economy. We estimate a Markov switching VAR for the euro area and the US, including additionally GDP, CPI and a short-term interest rate. We find evidence for two distinct states in both regions....

The interaction of macroeconomic variables may change as the nominal shortterm interest rates approaches zero. In this paper, we propose an empirical model capturing these changing dynamics with a time-varying parameter vector autoregressive process. State-dependent parameters are determined by...

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...