There was substantial spatial variation in labor market outcomes in Brazil over the 1990's. In 2000, about one fifth of workers lived in apparently economically stagnant municipios where real wages declined but employment increased faster than the national population growth rate. More than one...

Employment responses to the COVID-19 crisis differed widely across German local labour markets at the beginning of the pandemic, with differences in short-time work rates of up to 20 percentage points. We show that digital capital, and to a lesser extent working-from-home, were essential for the...

The German economy is an important economic driver in the Euro-area in terms of gross domestic product, labour force and international integration. We provide a state of the art estimate of the German output gap between 1995 and 2022 and present a nowcasting scheme that accurately predicts the...

Data revisions to national accounts pose a serious challenge to policy decision making. Well-behaved revisions should be unbiased, small and unpredictable. This paper shows that revisions to German national accounts are biased, large and predictable. Moreover, using filtering techniques designed...

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a...

We present a comprehensive disaggregate approach for short-term forecasting economic activity in Germany by explicitly taking into account the supply or production side and the demand side of GDP. The GDP figures calculated by the two sides usually yield different results and the official GDP...

We study how millions of highly granular and weekly household scanner data combined with novel machine learning techniques can help to improve the nowcast of monthly German inflation in real time. Our nowcasting exercise targets three hierarchy levels of the official consumer price index. First,...

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling...

The severity function approach (abbreviated SFA) is a method of selecting adverse scenarios from a multivariate density. It requires the scenario user (e.g. an agency that runs banking sector stress tests) to specify a "severity function", which maps candidate scenarios into a scalar severity...

Temporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility...