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Model uncertainty and aggregated default probabilities: new evidence from Austria

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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 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 data set. Our findings suggest that factor prices like short-term interest rates (STIs) 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 model are fairly robust with respect to the choice of the BMA parameters.
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Keywords: Bayesian model averaging; C52; E37; E44; firm failure; model uncertainty; ridge regression

Document Type: Research Article

Affiliations: 1: Department of Applied Statistics, Johannes Kepler University Linz, Altenbergerstraße 69, 4040, Linz, Austria 2: Oesterreichische Nationalbank, Otto-Wagner-Platz 3, 1090, Vienna, Austria 3: Department of Finance, Accounting and Statistics, WU Wirtschaftsuniversität Wien, Welthandelsplatz 1, 1020, Vienna, Austria

Publication date: 2014-03-14

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