We show, for the first time, a causal effect of local economic growth on infant mortality. We use geo-referenced data for non-migrating mothers from 46 developing countries and 128 DHS survey rounds and combine it with nighttime luminosity data at a granular level. Using mother fixed effects we...

We show, for the first time, a causal effect of local economic growth on infant mortality. We use geo-referenced data for non-migrating mothers from 46 developing countries and 128 DHS survey rounds and combine it with nighttime luminosity data at a granular level. Using mother mixed effects we...

This paper investigates the potential of an infant intervention to improve life expectancy, contributing to emerging interest in the early life origins of chronic disease. We analyse a pioneering program trialled in Sweden in the 1930s, which provided information, support and monitoring of...

Pollution is a common byproduct of economic activity. Although policymakers should account for both the benefits and the negative externalities of polluting activities, it is difficult to identify those who are harmed and those who benefit from them. To overcome this challenge, our paper uses a...

This paper investigates the potential of an infant intervention to improve life expectancy, contributing to emerging interest in the early life origins of chronic disease. We analyse a pioneering program trialled in Sweden in the 1930s, which provided information, support and monitoring of...

Pollution is a common byproduct of economic activity. Although policymakers should account for both the benefits and the negative externalities of polluting activities, it is difficult to identify those who are harmed and those who benefit from them. To overcome this challenge, our paper uses a...

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...

Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System...