While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this paper, we merge elements from the literature on the construction and...

We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple...

When constructing unconditional point forecasts, both direct- and iterated-multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions (VAR) are far more common than simpler DMS models. This is despite...

The rising burden of disease counts as one of the most salient concerns of a warming climate. These risks are especially serious in populous, rapidly growing urban landscapes of low-income, tropical countries. Surat, located on the banks of the River Tapi, has temperature and humidity patterns...

In this note we provide simulation evidence on the size and power of tests of predictive ability described in Giacomini and White (2006). Our goals are modest but non-trivial. First, we establish that there exist data generating processes that satisfy the null hypotheses of equal finite-sample...

The paper focuses on the option price subdiffusive model under the unusual behavior of the market, when the price may not be changed for some time which is quite a common situation in the modern financial markets or during global crises. In the model, the risk-free bond motion and classical GBM...

Putting a price on carbon - with taxes or developing carbon markets - is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the...

This paper provides a comprehensive early warning system (EWS) that balances the classical signaling approach with the best-realized machine learning (ML) model for predicting fiscal stress episodes. Using accumulated local effects (ALE), we compute a set of thresholds for the most informative...

Climate change is an existential threat to the world economy, with complex, evolving and nonlinear dynamics that remain a source of great uncertainty. There is a bourgeoning literature on the economic impact of climate change, but research on how climate change affects sovereign risks is...

This paper introduces the AI Generated Index of Occupational Exposure (GENOE), a novel measure quantifying the potential impact of artificial intelligence on occupations and their associated tasks. Our methodology employs synthetic AI surveys, leveraging large language models to conduct...