Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis...

We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function. This function integrates high-frequency,...

This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model...

There is considerable debate about the sustainability of the hydraulic fracturing (HF) water cycle in North America. Recently, this debate has expanded to China, where HF activities continue to grow. Here, we provide a critical review of the HF water cycle in China, including water withdrawal...

With the development of electrochemical energy storage systems with intrinsic safety, zinc–nickel batteries (ZNBs) have attracted widespread attention. However, the poor cycling life and shelf life restrict their further application. Herein, a porous-structured “Ryomen Sukuna” (RS)...

Zn–Ni battery is prospective green energy storage device. However, Zn(OH)42–, the dissolved product of ZnO in the electrolyte, redistributes during the cycle, leading to the shape changing of the anode. This issue severely limits the lifespan of zinc anodes. Despite the success in improving...

Even though blockchain as a research topic incites broad academic interest, there is arguably a balkanization that occurred along the natural delineation between public-permissionless blockchains and private-permissioned blockchains. IS researchers, for instance, have been more inclined to...

We use a unique Brazilian dataset on daily survey expectations to obtain direct measures of shocks to central bank target rates and changes in economic uncertainty. Using these measures, we gauge the effect of monetary policy shocks on economic uncertainty, term premia, inflation expectations,...

To answer this question, we develop new testing methods for identifying superior forecasting skills in settings with arbitrarily many forecasters, outcome variables, and time periods. Our methods allow us to address if any economists had superior forecasting skills for any variables or at any...