Macroeconomic Forecasting and Evaluation with Supervised and Neural Network Reinforced Factor Models

This thesis comprises three self-contained essays on macroeconomic forecasting with factor models, and on forecast evaluation tests. First, it is analyzed how factor estimates can be tailored to forecasting applications by incorporating the forecasting target directly in the factor estimation process. For this purpose, the Principal Covariate Regression technique is refined and it is analyzed under which circumstances gains in forecast accuracy can be achieved by integrating this form of supervision in the factor estimation. Second, the statistical factor model is aligned with the variational autoencoder framework in the context of macroeconomic forecasting. It is studied whether factor models enriched by neural networks can provide superior forecasting power for macroeconomic time series. In contrast to the original factor model, the resulting neural network reinforced factor model is not subject to the linearity restriction anymore, and can capture nonlinear common dynamics in the set of candidate predictors as well. Furthermore, it is proposed to incorporate the aforesaid supervision aspect within these models. The extended factor models are applied to forecast key monthly macroeconomic variables such as industrial production, inflation, and employment. The findings suggest that their forecasting capability can be significantly improved by the analyzed and refined extensions. Third, an adjustment of the Diebold and Mariano test is proposed. A comparison of two competing forecasts of the same economic quantity requires a formal statistical procedure to distinguish between a better predictive accuracy by coincidence and a fundamental advantage of one over the other. To this end, one of the most popular statistics is the Diebold and Mariano test. This thesis contributes to the literature by showing how the power of the Diebold and Mariano test can be improved when the forecasts are rational, i.e., unbiased and efficient

Rethinking energy, climate and security: a critical analysis of energy security in the US

Understanding the complicated relationship between energy, climate and security is vital both to the study of international relations and to ensure the continued survival of a world increasingly threatened by environmental change. Climate change is largely caused by burning fossil fuels for energy, but while discussions on the climate consider the role of energy, energy security debates largely overlook climate concerns. This article traces the separation between energy and climate through an analysis of US energy security discourse and policy. It shows that energy security is continually constructed as national security, which enables very particular policy choices and prioritises it above climate concerns. Thus, in many cases, policies undertaken in the name of energy security contribute directly to climate insecurity. The article argues that the failure to consider securing the climate as inherently linked to energy security is not just problematic, but, given global warming, potentially harmful. Consequently, any approach to dealing with climate change has to begin by rethinking energy security and security more broadly, as national (energy) security politics no longer provides security in any meaningful sense

Forecasting with supervised factor models

A conventional approach to forecast in a data-rich environment is to estimate factor-augmented predictive regressions with factors constructed by principal component analysis. This study analyzes under which circumstances gains in forecast accuracy can be achieved by incorporating some form of supervision in the factor estimation process. Specifically, principal covariate regression (PCovR) is considered. For the problem of choosing a value for the supervision parameter in PCovR, an information criterion is proposed. The information criterion is shown to be an appropriate means to find a good balance between predictor space compression and target orientation of the estimated factors. A simulation study and an empirical application on a macroeconomic dataset show that supervised factors can improve the forecasting accuracy of factor models

Ingestion and toxicity of polystyrene microplastics in freshwater bivalves

he ubiquity of microplastics in aquatic ecosystems has raised concerns over their interaction with biota. However,microplastics research on freshwater species, especially mollusks, is still scarce. We, therefore, investigated the factorsaffecting microplastics ingestion in the freshwater musselDreissena polymorpha. Using polystyrene spheres (5, 10, 45,90μm), we determined the body burden of microplastics in the mussels in relation to 1) exposure and depuration time, 2)body size, 3) food abundance, and 4) microplastic concentrations.D. polymorpharapidly ingested microplastics and ex-creted most particles within 12 h. A few microplastics were retained for up to 1 wk. Smaller individuals had a higher relativebody burden of microplastics than larger individuals. The uptake of microplastics was concentration‐dependent, whereas anadditional food supply (algae) reduced it. We also compared the ingestion of microplastics byD. polymorphawith 2 otherfreshwater species (Anodonta anatina,Sinanodonta woodiana), highlighting that absolute and relative uptake depends onthe species and the size of the mussels. In addition, we determined toxicity of polystyrene fragments (≤63μm,6.4–100 000 p mL–1) and diatomite (natural particle, 100 000 p mL–1)inD. polymorphaafter 1, 3, 7, and 42 d of exposure,investigating clearance rate, energy reserves, and oxidative stress. Despite ingesting large quantities, exposure to poly-styrene fragments only affected the clearance rate ofD. polymorpha. Further, results of the microplastic and diatomiteexposure did not differ significantly. Therefore,D. polymorphais unaffected by or can compensate for polystyrene fragmenttoxicity even at concentrations above current environmental levels.Environ Toxicol Chem2021;40:2247–2260. © 2021 TheAuthors.Environmental Toxicology and Chemistrypublished by Wiley Periodicals LLC on behalf of SETAC.Keywords:Microplastics; Toxic effects; Mollusk toxicit

Cryo-EM structure of cell-free synthesized human histamine 2 receptor/Gs complex in nanodisc environment

Abstract Here we describe the cryo-electron microscopy structure of the human histamine 2 receptor (H2R) in an active conformation with bound histamine and in complex with Gs heterotrimeric protein at an overall resolution of 3.4 Å. The complex was generated by cotranslational insertion of the receptor into preformed nanodisc membranes using cell-free synthesis in E. coli lysates. Structural comparison with the inactive conformation of H2R and the inactive and Gq-coupled active state of H1R together with structure-guided functional experiments reveal molecular insights into the specificity of ligand binding and G protein coupling for this receptor family. We demonstrate lipid-modulated folding of cell-free synthesized H2R, its agonist-dependent internalization and its interaction with endogenously synthesized H1R and H2R in HEK293 cells by applying a recently developed nanotransfer technique