Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265225 
Year of Publication: 
2022
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2022-31
Publisher: 
Bank of Canada, Ottawa
Abstract: 
This study proposes a Bayesian semiparametric binary response model using Markov chain Monte Carlo algorithms since this Bayesian algorithm works when the maximum likelihood estimation fails. Implementing graphic processing unit computing improves the computation time because of its efficiency in estimating the optimal bandwidth of the kernel density. The study employs simulated data and Monte Carlo experiments to compare the performances of the parametric and semiparametric models. We use mean squared errors, receiver operating characteristic curves and marginal effects as model assessment criteria. Finally, we present an application to evaluate the consumer bankruptcy rates based on Canadian TransUnion data.
Subjects: 
Econometric and statistical methods
Credit risk management
JEL: 
C1
C14
C35
C51
C63
D1
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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