Model Uncertainty and Aggregated Default Probabilities: New Evidence from Austria

Paul Hofmarcher, Stefan Kerbl, Bettina Grün, Michael Sigmund, Kurt Hornik

Publication: Working/Discussion PaperWU Working Paper


Understanding the determinants of aggregated default probabilities (PDs) has attracted substantial research over the past decades. This study addresses two major difficulties in understanding the determinants of aggregate PDs: Model uncertainty and multicollinearity among the regressors. We present Bayesian Model Averaging (BMA) as a powerful tool that overcomes model uncertainty. Furthermore, we supplement BMA with ridge regression to mitigate multicollinearity. We apply our approach to an Austrian dataset. Our findings suggest that factor prices like short term interest rates and energy prices constitute major drivers of default rates, while firms' profits reduce the expected number of failures. Finally, we show that the results of our baseline model are fairly robust to the choice of the prior model size.
Original languageEnglish
Publication statusPublished - 1 Oct 2012

Publication series

NameResearch Report Series / Department of Statistics and Mathematics

WU Working Paper Series

  • Research Report Series / Department of Statistics and Mathematics

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