The European debt crisis and fiscal reactions in Europe 2000–2014

After the outbreak of the global financial crisis, some governments in the EU experienced serious fiscal problems, while others were less affected. This paper seeks to shed light on the divergent fiscal performance in the EU countries before and after the outbreak of the crisis. Fiscal reaction functions of the primary balance are estimated for different groups of EU countries using quarterly data for the pre-crisis period 2001–2008 and for the crisis period 2009–2014. The pre-crisis estimations reveal some differences in persistence and cyclical reaction between different groups of countries, but in most cases little feedback from the debt stock to the primary balance. The fiscal reaction functions of the countries that eventually developed fiscal problems do not stand out. The estimations on data from the crisis period show largely unchanged persistence and counter-cyclicality but much more feedback from the debt stock, and this applies both to the crisis countries and those less affected. In spite of large deficits and accumulation of debt, the underlying fiscal reaction has become more prudent after the outbreak of the European debt crisis.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.