## Structural Modeling and Forecasting Using a Cluster of Dynamic Factor Models (with C.Glocker)

We propose a modeling approach involving a series of small-scale dynamic factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. This approach merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent nowcasting and forecasting on a larger scale. While the CDFM has a simple structure and is easy to replicate, its forecasts are more precise than those of a wide range of competing models and those of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.

## Estimating the Probability of Acting as a Trustee (with D.Stadelmann)

We discuss a binomial mixture model for estimating the probability of a political representative acting as a delegate or a trustee. The model also returns the probability of congruence of a representative with the national median voter. The estimated probability of congruence strongly correlates with the observed frequency of congruence, which was obtained by matching parliamentary roll-call votes with the will of the median voter revealed in Swiss national referendums on identical legislative proposals. Since our method uses the roll-call votes of political representatives as sole input, it can be used to infer congruence levels of politicians, even if the will of the median voter is unobserved.