Dependent Dirichlet Process Rating Model (DDP-RM)

Typical IRT rating-scale models assume that the rating category threshold parameters are the same over examinees. However, it can be argued that many rating data sets violate this assumption. To address this practical psychometric problem, we introduce a novel, Bayesian nonparametric IRT model for rating scale items. The model is an infinite-mixture of Rasch partial credit models, based on a localized Dependent Dirichlet process (DDP). The model treats the rating thresholds as the random parameters that are subject to the mixture, and has (stick-breaking) mixture weights that are covariate-dependent. Thus, the novel model allows the rating category thresholds to vary flexibly across items and examinees, and allows the distribution of the category thresholds to vary flexibly as a function of covariates. We illustrate the new model through the analysis of a simulated data set, and through the analysis of a real rating data set that is well-known in the psychometric literature. The model is shown to have better predictive-fit performance, compared to other commonly used IRT rating models.Comment: 2 tables and 5 figure

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper, we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.Credit ratings, probit, state dependence

Evaluating Yield Models for Crop Insurance Rating

Generated crop insurance rates depend critically on the distributional assumptions of the underlying crop yield loss model. Using farm level corn yield data from 1972-2008, we revisit the problem of examining in-sample goodness-of-fit measures across a set of flexible parametric, semi-parametric, and non-parametric distributions. Simulations are also conducted to investigate the out-of-sample efficiency properties of several competing distributions. The results indicate that more parameterized distributional forms fit the data better in-sample due to the fact that they have more parameters, but are generally less efficient out-of-sample–and in some cases more biased–than more parsimonious forms which also fit the data adequately, such as the Weibull. The results highlight the relative advantages of alternative distributions in terms of the bias-efficiency tradeoff in both in- and out-of-sample frameworks.Yield distributions, Crop Insurance, Weibull Distribution, Beta Distribution, Mixture Distribution, Out-of-Sample Efficiency, Goodness-of-Fit, Insurance Rating Efficiency, Farm Management, Financial Economics, Land Economics/Use,

Credit rating and bank behavior in India: Possible implications of the new Basel accord

The paper examines the impact of credit rating on capital adequacy ratios of Indian state-owned banks using quarterly data for the period 1997:1 to 2002:4. To this end, a multinomial logit model with multi credit rating indicators as dependent variable is estimated. The variables that can impinge upon capital adequacy ratio have been used as explanatory variables. Two separate models — one for long-term credit rating and another for short-term credit rating—have been estimated. The paper concludes that, both for short-term as well as for long-term ratings, capital adequacy ratios are an important factor impinging on credit rating of Indian state-owned banks.banknig; capital adequacy; credit rating; multinomial logit model; India