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...

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...

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...

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...

Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix to associate units or series with those...

It is common to transform data to stationarity, such as by differencing and demeaning, before estimating factor models in macroeconomics. Imposing these transformations, however, limit opportunities to learn about trending behaviour. Trends and deterministic processes can play a central role in...

We use the geometric representation of factor models to represent the factor loading structure by sets corresponding to unit-specific non-zero loadings. We formulate global and local identification conditions based on set conditions. We propose two algorithms to efficiently evaluate Sato...

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...