This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample...

This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001-2015. Design/methodology/approach: The methodology involves constructing model scores for...

Housing prices in many countries have increased significantly over the past years, fueling a fear that speculative price bubbles will return. However, it can be difficult for policymakers to recognize when regulatory interventions in the market are necessary to counteract bubbles. This report...

Macroeconomic imbalances increase the vulnerability of an economy to adverse shocks, which in turn can lead to crises with severe economic and social costs. We propose an early warning model that predicts such crises. We identify a set of macroeconomic indicators capturing domestic and external...

This analytical work assesses the current capabilities of, as well as the gaps and challenges faced by, the Georgian Hydrometeorological Department (GHMD) in producing and delivering weather, climate, and hydrological (hydrometeorological) products and services based on users' needs. It provides...