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A Mixed Ordered Probit Analysis of Corporate Credit Ratings

50 Pages Posted: 28 Jan 2014  

Haran Segram

NYU Stern School of Business

Date Written: August 30, 2013

Abstract

Using a U.S. entity credit ratings dataset, I examine the performance of Mixed (Random Parameters) Ordered Probit (MOP) model with more conventional Standard (Fixed Parameters) Ordered Probit (SOP) model in analysis and explanation of corporate credit ratings. Consistent with the discrete choice literature, I find that a MOP model is better able to extract, to a fuller extent, the underlying behavioral information in the model covariates than SOP. I show that the likelihood ratio test rejects the SOP model in favor of the MOP model. The understanding of behavioral responsiveness of variations in levels of model covariates on probability outcome (i.e. levels of credit ratings) is significant for economic decisions.

Keywords: Corporate Credit Ratings, Mixed Logit, Random Parameters, Unobserved Heterogeneity, Systematic Preference Heterogeneity

Suggested Citation

Segram, Haran, A Mixed Ordered Probit Analysis of Corporate Credit Ratings (August 30, 2013). Available at SSRN: https://ssrn.com/abstract=2385748 or http://dx.doi.org/10.2139/ssrn.2385748

Haran Segram (Contact Author)

NYU Stern School of Business ( email )

Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States

HOME PAGE: http://www.stern.nyu.edu/faculty/bio/haran-segram

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