Understanding the determinants of aggregated corporate default probabilities (PDs) has attracted substantial research interest over the past decades. This study addresses two major difficulties in understanding the determinants of aggregate PDs: model uncertainty and multicollinearity among the...

Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates. We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining...

Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus...