We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables---outnumbering the total time-series...

Im Zentrum dieser Dissertation steht das Beschreiben und Erklären von Konjunkturdynamiken. Motiviert durch den außerordentlich starken wirtschaftlichen Einbruch in 2008/2009 betont die Arbeit dabei die Wichtigkeit der Nutzung von nichtlinearen Modellansätzen. Die Dissertation kann als Beitrag...

We study the effects of an annuity market imperfection on individual agents' labour supply and retirement decisions and on the macroeconomic growth rate in an overlapping generations model with endogenous growth. We model imperfect annuities by introducing a load factor on the interest rate...

This paper studies how the interplay between technological shocks and financial variables shapes the properties of macroeconomic dynamics. Most of the existing literature has based the analysis of aggregate macroeconomic regularities on the representative agent hypothesis (RAH). However, recent...

A reason for revising the EU fiscal rules in the early 2010s was to improve member states' forecasts against a background of documented biases in official projections. Using data from Stability and Convergence Programmes and the European Commission's Spring Forecasts, evidence is presented which...

The ability of term spread to forecast U.S. output growth could be improved by two ways: (i) Combining with the Harrod-Domar variable - net saving as a percentage of gross national income - that used to proxy for aggregate supply; and (ii) Using a system of simultaneous equations, in which U.S....

Modern calculation of textual sentiment involves a myriad of choices for the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and weighting of...

We use Bayesian additive regression trees to reexamine the efficiency of growth and inflation forecasts for Germany. To this end, we use forecasts of four leading German economic research institutes for the sample period from 1970 to 2016. We reject the strong form of forecasts efficiency and...

To examine whether including economic data on other countries could improve the forecast of U.S. GDP growth, we construct a large data set of 77 countries representing over 90 percent of global GDP. Our benchmark model is a dynamic factor model using U.S. data only, which we extend to include...