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...

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...

Recent research has found that macroeconomic survey forecasts of uncertainty exhibit several deficiencies, such as horizon-dependent biases and lower accuracy than simple unconditional uncertainty forecasts. We examine the inflation uncertainty forecasts from the Bank of England, the Banco...

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...

Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM,...

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...