The relevance of irrelevance in brand communication

Brand managers strive to achieve an outstanding position in the psyche of the user by differentiating the product and service. In order to do so, brands are now often promoted by communications that focus on a trivial attribute difference. The current study tests both how the use of such an irrelevant attribute affects the perceptions of the consumer and how they rate the brand when the irrelevance of the attribute is previously revealed. The results of a controlled experiment (n = 894) show that the use of irrelevant attributes generally has a positive effect on buying behavior and that this effect is obtained even when the actual irrelevance is previously proven to the consumer. Further, the results are consistent across a variety of outcome variables, including attention, perceived uniqueness, price fairness, attitude toward the brand, and intention to buy the brand. © 2010 Wiley Periodicals, Inc.     

The wages of BMI: Bayesian analysis of a skewed treatment–response model with nonparametric endogeneity

We generalize the specifications used in previous studies of the effect of body mass index (BMI) on earnings by allowing the potentially endogenous BMI variable to enter the log wage equation nonparametrically. We introduce a Bayesian posterior simulator for fitting our model that permits a nonparametric treatment of the endogenous BMI variable, flexibly accommodates skew in the BMI distribution, and whose implementation requires only Gibbs steps. Using data from the 1970 British Cohort Study, our results indicate the presence of nonlinearities in the relationships between BMI and log wages that differ across men and women, and also suggest the importance of unobserved confounding for our sample of males. Copyright © 2008 John Wiley & Sons, Ltd.     

Interne Revision &; Controlling — Instrumente moderner Unternehmensführung in Kooperation

Ohne Zusammenfassung     

Credit Risk Models – Do They Deliver Their Promises? A Quantitative Assessment

We develop a framework to assess the statistical significance of expected default frequency calculated by credit risk models. This framework is then used to analyse the quality of two commercially available models that have become popular among practitioners: KMV Credit Monitor and RiskCalc from Moody's.
Using a unique database of expected default probability from both vendors, we study both the consistency of the prediction and its timeliness. We introduce the concept of cumulative accuracy profile (CAP) that allows to see in one curve the percentage of defaulting companies captured by the models one year in advance. We also use the Miller's information test to see if the models add information to the S&P rating.
The result of the analysis indicates that these models indeed add relevant information not accounted for by rating alone. Moreover, with respect to rating agencies, the models predict defaults more than ten months in advance on average.
(J.E.L.: C52).