This paper studies the interplay between climate, health, and the economy in a stylized world with four heterogeneous regions, labeled ‘West' (cold and rich), ‘China' (cold and poor), ‘India' (warm and poor), and ‘Africa' (warm and very poor). We introduce health impacts into a simple...

This paper studies the interplay between climate, health, and the economy in a stylized world with four heterogeneous regions, labeled ‘West' (cold and rich), ‘China' (cold and poor), ‘India' (warm and poor), and ‘Africa' (warm and very poor). We introduce health impacts into a simple...

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these...

Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical growth model, many new (endogenous) growth models have been proposed. This causes a lack of robustness of the parameter estimates and makes the determination of the key determinants of growth...

In this paper we shall be interested in two questions on extremes relating to world records in athletics. The first question is: what is the ultimate world record in a specific athletics event (such as the 100m for men or the high jump for women), given today's state of the art? Our second...

Overconfidence seems to be an essential aspect of human nature, and one way to study overconfidence is to consider students' forecasts of their exam grades. Part of a student's grade expectation is based on the student's previous academic achievements; what remains can be interpreted as...

The weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model averaging estimators when accounting for problems of...

Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable' prior as one which leads to a continuous and suitable analog to the pretest...

This papers offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice...

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have...