@misc{Ptak-Chmielewska_Aneta_Statistical_2018,
 author={Ptak-Chmielewska, Aneta and Kuleta, Piotr},
 identifier={DOI: 10.15611/eada.2018.1.07},
 year={2018},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Econometrics = Ekonometria, 2018, Vol. 22, No. 1, s. 94-106},
 language={eng},
 abstract={Default risk assessment is crucial in the banking activity. Different models were developed in the literature using the discriminant analysis, logistic regression and data mining techniques. In this paper the logistic regression was applied to verify models proposed by R. Jagiełło for different sectors. As an alternative, the logistic regression model with the nominal variable SECTOR was applied on the pooled sample of enterprises. The dynamic approach using the Cox regression survival model was estimated. Including the nominal variable SECTOR only slightly increases the predictive power of the model (in the case of “defaults”). The predictive power of the Cox regression model is lower, the only advantage is the higher accuracy classification in the case of “defaulted” enterprises},
 title={Statistical models in enterprises default risk assessment – an example of application},
 type={artykuł},
 keywords={default risk, logistic regression, Cox model, ryzyko default, regresja logistyczna, model Coxa},
}