Social comparison nudges that employ descriptive norms were found to increase charitable giving. This paper finds that individuals who receive a descriptive norm donate significantly more when they have to guess the descriptive norm beforehand. We argue that guessing draws attention to the norm...

The linear pool is the most popular method for combining density forecasts. We analyze its implications concerning forecast uncertainty, using a new framework that focuses on the means and variances of the individual and combined forecasts. Our results show that, if the variance predictions of...

Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate...

This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and...

The linear pool is the most popular method for combining density forecasts. We analyze the linear pool's implications concerning forecast uncertainty in a new theoretical framework that focuses on the mean and variance of each density forecast to be combined. Our results show that, if the...

Motivated by the Basel 3 regulations, recent studies have considered joint forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring functions can be used to evaluate forecast performance in this context. However, little intuitive or empirical guidance is currently available,...

In many empirical applications, a combined density forecast is constructed using the linear pool which aggregates several individual density forecasts. We analyze the linear pool in a mean/variance prediction space setup. Our theoretical results indicate that a well-known 'disagreement' term can...

Several recent surveys ask for a person's subjective probabilities that the in- ation rate falls into various outcome ranges. We provide a new measure of the uncertainty implicit in such probabilities. The measure has several advantages over existing methods: It is trivial to implement, requires...

Several recent surveys ask for a person's subjective probabilities that the inflation rate falls into various outcome ranges. We provide a new measure of the uncertainty implicit in such probabilities. The measure has several advantages over existing methods: It is robust, trivial to implement,...

In Bayesian inference, predictive distributions are typically in the form of samples generated via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic analysis of how to make and evaluate probabilistic forecasts from such simulation output. Based on proper...