We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density
forecasting through a novel neural network architecture with dedicated mean and variance …

Predicting the future is notoriously difficult, even more so during crises when the realizations
of economic variables are far from their average. In fact, econometric models are typically …

We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density
forecasting through a novel neural network architecture with dedicated mean and variance …

This paper introduces non-linear dimension reduction in factor-augmented vector
autoregressions to analyze the effects of different economic shocks. I argue that controlling …

The crisis periods of the past decades have highlighted the difficulty of forecasting economic
indicators due to increased non-linearity and rapidly changing dynamics. To address this …

The crisis periods of the past decades have highlighted the difficulty of forecasting economic
indicators due to increased non-linearity and rapidly changing dynamics. To address this …