In bank stress tests, the role of a satellite model is to tie bank-specific risk variables to macroeconomic variables that can generate stress. For valid stress tests it is important to develop a comprehensive satellite model that both preserves the sense of known economic relationships and also...

Credit risk modeling remains an important research topic both for financial institutions and the academic community due to its significant contribution to the issue of a bank’s capital adequacy. In this paper we build macro models for the default rates of Greek bank’s loan portfolios....

Gun violence is the most pressing public safety problem in American cities. We report results from a randomized controlled trial (N=2,456) of a community-researcher partnership--the Rapid Employment and Development Initiative (READI Chicago)--which provided 18 months of a supported job alongside...

This paper analyzes the performance of temporal fusion transformers in forecasting realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing the predictions with those of state-of-the-art machine learning methods as well as GARCH models. The models are trained on...

Panel data are rarely available for developing countries. Departing from traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty mobility at the cohort level, we develop a procedure that works with as few as two survey rounds and produces point...

Panel data are rarely available for developing countries. Departing from traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty mobility at the cohort level, we develop a procedure that works with as few as two survey rounds and produces point...

We analyze the usefulness of accounting information on tax loss carryforwards and negative performance to predict earnings and cash flows. We use hand-collected information on tax loss carryforwards and the corresponding deferred taxes from the International Financial Reporting Standards tax...

Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate...

This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a...

This paper documents a comparative application of algorithms to deal with the problem of missing values in higher frequency data sets. We refer to Swiss business tendency survey (BTS) data, in particular the KOF manufacturing surveys, which are conducted in both monthly and quarterly frequency,...