The traditional consumer price index is often produced at an aggregate level, using data from few, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where large populations may reside in fragile situations. Traditional price data collection...

Motivated by the deterioration in global food security conditions, this paper develops a parsimonious machine learning model to derive a multi-year outlook of global severe food insecurity from macro-economic projections. The objective is to provide forecasts that are internally consistent with...

Capabilities to track fast-moving economic developments re-main limited in many regions of the developing world. This complicates prioritizing policies aimed at supporting vulnerable populations. To gain insight into the evolution of fluid events in a data scarce context, this paper explores the...

This paper introduces a new model for spatial time series in which cross-sectional dependence varies nonlinearly over space by means of smooth transitions. We refer to our model as the Smooth Transition Spatial Autoregressive (ST-SAR). We establish consistency and asymptotic Gaussianity for the...

Recent advances in food insecurity classification have made analytical approaches to predict and inform response to food crises possible. This paper develops a predictive, statistical framework to identify drivers of food insecurity risk with simulation capabilities for scenario analyses, risk...

Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and...

The fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate...

This paper introduces a Spatial Vector Autoregressive Moving Average (SVARMA) model in which multiple cross-sectional time series are modeled as multivariate, possibly fat-tailed, spatial autoregressive ARMA processes. The estimation requires specifying the cross-sectional spillover channels...

This paper revisits the issue of environment and development raised in the 1992 World Development Report, with new analysis tools and data. The paper discusses inference and interpretation in a machine learning framework. The results suggest that production gradually favors conserving the...

This paper introduces a new model for spatial time series in which cross-sectional dependence varies nonlinearly over space by means of smooth transitions. We refer to our model as the Smooth Transition Spatial Autoregressive (ST-SAR). We establish consistency and asymptotic Gaussianity for the...