In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on the characteristic function of the...

In a model inspired by neuroscience, we show that constrained optimal perception encodes lottery rewards using an S-shaped encoding function and over-samples low-probability events. The implications of this perception strategy for behavior depend on the decision-maker’s understanding of the...

We use a field experiment and a within-subject design based on multiple Choice Lists (CLs) that integrate time and risk. Diminishing impatience with extended time horizons is studied by varying time horizons from one week to two years. Time-dated risky prospects are constant within CLs and are...

The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of...

In the Corona pandemic, it became clear with burning clarity how much good quality statistics are needed, and at the same time how unsuccessful we are at providing such statistics despite the existing technical and methodological possibilities and diverse data sources. It is therefore more than...

Realized covariance matrices (RCs) are an important input to asses the risks involved in different investment allocations and it is thus useful to model and forecast them. To this end generalized autoregressive score (GAS) models are employed in this paper. These models are ideal for comparing...

In first-price sealed-bid auctions, a power probability weighting function is observationally equivalent to a model of constant absolute risk aversion. By comparing auctions with different ceilings on a computerized opponent’s bid space, we can separate subjective probability weighting and...

We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes....