We reconsider the relationship between income and health taking a distributional perspective rather than one centered on conditional expectation. Using structured additive distributional regression, we find that the association between income and health is larger than generally estimated because aspects of the conditional health distribution that go beyond the expectation imply worse outcomes for those with lower incomes. Looking at German data from the Socio-Economic Panel, we find that the risk of bad health is roughly halved when doubling the net equivalent income from 15,000 to 30,000€. This is more than tenfold of the magnitude of change found when considering expected health measures. A distributional perspective thus highlights another dimension of the income-health relation-that the poor are in particular faced with greater health risk at the lower end of the health distribution. We therefore argue that when studying health outcomes, a distributional approach that considers stochastic variation among observationally equivalent individuals is warranted.

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This study addresses the much-discussed issue of the relationship between health and income. In particular, it focuses on the relation between mental health and household income by using generalized additive models of location, scale and shape and thus employing a distributional perspective. Furthermore, this study aims to give guidelines to applied researchers interested in taking a distributional perspective on health inequalities. In our analysis we use cross-sectional data of the German socioeconomic Panel (SOEP). We find that when not only looking at the expected mental health score of an individual but also at other distributional aspects, like the risk of moderate and severe mental illness, that the relationship between income and mental health is much more pronounced. We thus show that taking a distributional perspective, can add to and indeed enrich the mostly mean-based assessment of existent health inequalities.

Predicting the trend of stock prices is a central topic in financial engineering. Given the complexity and nonlinearity of the underlying processes we consider the use of neural networks in general and sentiment analysis in particular for the analysis of financial time series. As one of the biggest social media platforms with a user base across the world, Twitter offers a huge potential for such sentiment analysis. In fact, stocks themselves are a popular topic in Twitter discussions. Due to the real-time nature of the collective information quasi contemporaneous information can be harvested for the prediction of financial trends. In this study, we give an introduction in financial feature engineering as well as in the architecture of a Long Short-Term Memory (LSTM) to tackle the highly nonlinear problem of forecasting stock prices. This paper presents a guide for collecting past tweets, processing for sentiment analysis and combining them with technical financial indicatorsto forecast the stock prices of Apple 30m and 60m ahead. A LSTM with lagged close price is used as a baseline model. We are able to show that a combination of financial and Twitter features can outperform the baseline in all settings. The code to fully replicate our forecasting approach is available in the Appendix.

Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.