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We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time.

We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced-form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation-by-equation estimation. Second, we apply recently developed global-local shrinkage priors to the VAR coefficients to cure the curse of dimensionality. Third, we utilize recent innovations to efficiently sample from high-dimensional multivariate Gaussian distributions. This makes simulation-based fully Bayesian inference feasible when the dimensionality is large but the time series length is moderate. We demonstrate the merits of our approach in an extensive simulation study and apply the model to US macroeconomic data to evaluate its forecasting capabilities.

Science education is a crucial issue with long-term impacts for Europe as the low enrolment rates in the STEM-fields, including (natural) science, technology, engineering and mathematics, will lead to a workforce problem in research and development. In order to address this challenge, the EU-funded research project SciChallenge (project.scichallenge.eu) aims to find a new way for getting young people more interested in STEM. For this purpose, the project developed and implemented a social-media-based STEM-contest for young people, which aims at increasing the attractiveness of science education and careers among young people. In the first two parts, the paper reflects on the problem, introduces the project and highlights the main steps of the preparation of the contest. The third section of the paper presents the idea, design and implementation of the digital contest platform (www.scichallenge.eu), which serves as the core of the challenge. The fourth part of the paper will provide a status update on the contest pilot. It will provide a summary of the experiences that the consortium made with this novel approach as well as the main obstacles that the consortium was facing. The paper will conclude with a preliminary reflection on the question if such an approach can help to increase the interest of young people in STEM-education and careers., Comment: INTED 2017

This paper proposes a straightforward algorithm to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each coefficient in the system. We significantly decrease the computational burden by approximating the latent indicators that drive the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. The merits of our approach are illustrated with two applications. First, we forecast the US term structure of interest rates and demonstrate forecast gains of the proposed mixture innovation model relative to other benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening.

Science education will play a vital role in shaping the present and future of modern societies. Thus, Europe needs all its talents to increase creativity and competitiveness. Young boys and girls especially have to be engaged to pursue careers in Science, Technology, Engineering and Mathematics (STEM). However, statistics still show that enrolment rates in STEM-based degree programs are decreasing. This will lead to a workforce problem in the industrial sector as well as in research and development, especially in many of the new member countries. This paper highlights a recently funded EU-research project SciChallenge (www.scichallenge.eu), which focuses on the development of novel concepts to get young people excited about science education. It uses a contest-based approach towards self-produced digital education materials from young people for young people. In cooperation with partner schools, teachers, and other youth-oriented institutions, the contest participants (individuals or groups) between the ages of 10 and 20 years generate creative digital materials (videos, slides, or infographics). The participants upload their content in social media channels and the submissions are aggregated on the SciChallenge Web Platform. The winners receive prizes funded by science-oriented industry and other stakeholders. Intelligent cross-sectoral positioning of various awareness modules on the SciChallenge Open Information Hub is expected to increase awareness on science careers. Through a strong involvement of related organizations and industries, we expect to open new opportunities for young people in regards to internships or taster days in STEM disciplines. Additionally, aggregated information on science events (such as, slams, nights, festivals) is shared. With this multi-level approach, SciChallenge may boost the attractiveness of science education and careers among young people., Comment: New Perspectives in Science Education, 5th Edition, 17 - 18 March 2016, Florence, Italy

The complexity of quantum many-body systems originates from the interplay of strong interactions, quantum statistics, and the large number of quantum-mechanical degrees of freedom. Probing these systems on a microscopic level with single-site resolution offers important insights. Here we report site-resolved imaging of two-component fermionic Mott insulators, metals, and band insulators using ultracold atoms in a square lattice. For strong repulsive interactions we observe two-dimensional Mott insulators containing over 400 atoms. For intermediate interactions, we observe a coexistence of phases. From comparison to theory we find trap-averaged entropies per particle of $1.0\,k_{\mathrm{B}}$. In the band-insulator we find local entropies as low as $0.5\,k_{\mathrm{B}}$. Access to local observables will aid the understanding of fermionic many-body systems in regimes inaccessible by modern theoretical methods., Comment: 6+7 pages

We demonstrate site-resolved imaging of individual fermionic lithium-6 atoms in a 2D optical lattice. To preserve the density distribution during fluorescence imaging, we simultaneously cool the atoms with 3D Raman sideband cooling. This laser cooling technique, demonstrated here for the first time for lithium-6 atoms, also provides a pathway to rapid low-entropy filling of an optical lattice. We are able to determine the occupation of individual lattice sites with a fidelity >95%, enabling direct, local measurement of particle correlations in Fermi lattice systems. This ability will be instrumental for creating and investigating low-temperature phases of the Fermi-Hubbard model, including antiferromagnets and d-wave superfluidity., Comment: 5 pages, 4 figures, added references