Collecting data for the analysis of past human rights violations is fraught with challenges. For example, individuals from or about whom data should be collected may be displaced, missing, or dead. Some reports of acts may be easier to find than others, and as a result, datasets will be biased...

Summary We consider estimating a probability density p based on a random sample from this density by a Bayesian approach. The prior is constructed in two steps, by first constructing priors on a collection of models each expressing a qualitative prior guess on the true density, and next...

Abstract This paper starts from the observation that parameter instability and model uncertainty are relevant problems for the analysis of monetary policy in small macroeconomic models. We propose to deal with these two problems by implementing a novel "thick recursive modelling" approach. At...

Abstract When studying the causal effect of x on y , researchers may conduct regression and report a confidence interval for the slope coefficient ${\beta }_{x}$ . This common confidence interval provides an assessment of uncertainty from sampling error, but it does not assess uncertainty from...

Summary A popular argument about economic policy under uncertainty states that decentralisation offers the possibility to learn from local or regional policy experiments. Often, an analogy between market competition as a discovery procedure and political competition is used to corroborate this...

Abstract It is standard in quantitative risk management to model a random vector ${\mathbf {X}:=\lbrace X_{t_k}\rbrace _{k=1,\ldots ,d}}$ of consecutive log-returns to ultimately analyze the probability law of the accumulated return ${X_{t_1}+\cdots +X_{t_d}}$ . By the Markov regression...

Abstract Numerical challenges inherent in algorithms for computing worst Value-at-Risk in homogeneous portfolios are identified and solutions as well as words of warning concerning their implementation are provided. Furthermore, both conceptual and computational improvements to the Rearrangement...

Summary Robust utility functionals arise as numerical representations of investor preferences, when the investor is uncertain about the underlying probabilistic model and averse against both risk and model uncertainty. In this paper, we study the duality theory for the problem of maximizing the...

SUMMARY We give an explicit PDE characterization for the solution of a robust utility maximization problem in an incomplete market model, whose volatility, interest rate process, and long-term trend are driven by an external stochastic factor process. The robust utility functional is defined in...

Purpose The purpose of this paper is to improve control performance and safety of a real two-wheeled inverted pendulum (TWIP) robot by dealing with model uncertainty and motion restriction simultaneously, which can be extended to other TWIP robotic systems. Design/methodology/approach The...