Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models

Xiangjin Shen, Iskander Karibzhanov, Hiroki Tsurumi and Shiliang Li

Staff Working Papers from Bank of Canada

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 bankrupt rates based on Canadian TransUnion data.

Keywords: Credit risk management; Econometric and statistical methods transmission (search for similar items in EconPapers)
JEL-codes: C1 C14 C35 C51 C63 D1 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2022-07
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:22-31

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