@techreport{Forstemann2011Improvements,
abstract = {Group-specific estimations can significantly improve the predictive power of accountingbased rating models. This is shown using a binary logistic regression model applied to the Deutsche Bundesbank's USTAN dataset, which contains 300,000 financial statements provided by German companies for the years 1994 to 2002, i. e. throughout a complete business-cycle. The robustness and the representability of this result is verified through out-of-sample tests and through comparisons with a benchmark model which applies the variables of Moody's RiskCalcTM for Germany.},
address = {Frankfurt am Main},
author = {Till F\"{o}rstemann},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
isbn = {978-3-86558-745-9},
keywords = {G21; G33; C52; 330; Credit Risk; Credit Rating; Probability of Default; Logistic Regression},
language = {eng},
number = {2011,11},
publisher = {Deutsche Bundesbank},
title = {Improvements in rating models for the German corporate sector},
type = {Discussion Paper Series 2: Banking and Financial Studies},
url = {http://hdl.handle.net/10419/50001},
year = {2011}
}
