OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests, poverty mapping or cross-survey imputations. This paper compares the performance of econometric and machine learning models in predicting poverty using alternative objective...

Poverty prediction models are used by economists to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, or vulnerability analyses. Based on the models used by this literature, this...

In this paper, we derive a modification of a forward-looking Taylor rule, which integrates two variables measuring the uncertainty of inflation and GDP growth forecasts into an otherwise standard New Keynesian model. We show that certainty-equivalence in New Keynesian models is a consequence of...

After hitting the lower bound on interest rates, the Eurosystem engaged in a public sector purchase programme (PSPP) and forward guidance (FG). We use prior and posterior predictive analysis to evaluate the importance of parameter uncertainty in an analysis of these policies. We model FG as an...

When generating conditional forecasts in dynamic models it is common to impose the conditions as restrictions on future structural shocks. However, these conditional forecasts often ignore that there may be uncertainty about the future development of the restricted variables. Our paper therefore...