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2002-10-17Buch DOI: 10.18452/3535
Exploring Credit Data
dc.contributor.authorMüller, Marlene
dc.contributor.authorHärdle, Wolfgang Karl
dc.date.accessioned2017-06-15T21:18:33Z
dc.date.available2017-06-15T21:18:33Z
dc.date.created2005-10-12
dc.date.issued2002-10-17
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4187
dc.description.abstractCredit scoring methods aim to assess the default risk of a potential borrower. This involves typically the calculation of a credit score and the estimation of the probability of default. One of the standard approaches is logistic discriminant analysis, also referred to as logit model. This model maps explanatory variables for the default risk to a credit score using a linear function. Nonlinearity can be included by using polynomial terms or piecewise linear functions. This may give however only a limited reflection of a truly nonlinear relationship. Moreover, an additional modeling step may be necessary to determine the optimal polynomial order or the optimal interval classification. This paper presents semiparametric extensions of the logit model which directly allow for nonlinear relationships to be part of the explanatory variables. The technique is based on the theory generalized partial linear models. We illustrate the advantages of this approach using a consumer retail banking data set.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc330 Wirtschaft
dc.titleExploring Credit Data
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10049513
dc.identifier.doihttp://dx.doi.org/10.18452/3535
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages17
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2002
local.edoc.container-issue79
local.edoc.container-year2002
local.edoc.container-erstkatid2135319-0

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