Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245268 
Year of Publication: 
2020
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 8 [Issue:] 1 [Publisher:] Taylor & Francis [Place:] Abingdon [Year:] 2020 [Pages:] 1-20
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
The purpose of this study is to assess model risk with respect to parameter estimation for a simple binary logistic regression model applied as a predictive model. The assessment is done by comparing the effectiveness of eleven different parameter estimation methods. The results from the historical credit dataset of a certain financial institution confirmed that using several optimization methods to address parameter estimation risk for predictive models is substantial. This is the case, especially when there exists a numerical optimization method that estimates the optimum parameters and minimizes the cost function among alternative methods. Our study only considers a univariate predictor with a static sample size of cases. This research work contributes to the literature by presenting different parameter estimation methods for predicting the probability of default through binary logistic regression model and determining optimum parameters that minimize the objective model's cost function. The Mini-Batch Gradient Descent method is revealed to be the better parameter estimator.
Subjects: 
logistic regression
model risk
parameter estimation
probability of default
JEL: 
C13
C52
C53
D81
G32
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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