Despite its importance for marketing channels research, the effect of interdependence on channel outcome variables remains elusive because of the inconsistent manner in which it is operationalized and the analytical methods used to assess its impact. To address those gaps, the authors first review prior approaches and identify the sources of their limitations. Then, the authors use the response surface approach (RSA) and derive three managerial insights that can be garnered from its use. They apply RSA to industrial distributor-supplier relationships and contrast it with previous methods. The empirical study results suggest that distributors perceive differential effects of supplier dependence and distributor dependence on outcome variables. The authors elaborate on managerial and research implications of the RSA results.

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We develop in this article a data-analytic method to forecast the severity of next record insured loss to property caused by natural catastrophic events. The method requires and employs the knowledge of an expert and accounts for uncertainty in parameter estimation. Both considerations are essential for the task at hand because the available data are typically scarce in extreme value analysis. In addition, we consider three-parameter Gamma priors for the parameter in the model and thus provide simple analytical solutions to several key elements of interest, such as the predictive moments of record value. As a result, the model enables practitioners to gain insights into the behavior of such predictive moments without concerning themselves with the computational issues that are often associated with a complex Bayesian analysis. A data set consisting of catastrophe losses occurring in the United States between 1990 and 1999 is analyzed, and the forecasts of next record loss are made under various prior assumptions. We demonstrate that the proposed method provides more reliable and theoretically sound forecasts, whereas the conditional mean approach, which does not account for either prior information or uncertainty in parameter estimation, may provide inadmissible forecasts. Copyright The Journal of Risk and Insurance.

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A forecasting model of next record value proposed by Hill (1) assumes the underlying distribution F(x) is of an algebraic functional form with a shape parameter α for large x. That is, 1 − F(x) C x −α , for large x. In this article, we extend his model by incorporating a three-parameter Gamma prior of α to derive analytical solutions of the predictive distribution and moments of X given that X is a new record value. These closed-form formulas can be represented as ratios of moments of Gamma distributions. We apply the proposed model to a real-life data set that consists of the insured property losses of 33 catastrophes caused by tropical storms in the United States in 1995. The example illustrates the importance of incorporating prior experience and accounting for uncertainty in parameter estimation when forecasting record values. Both considerations are the main ingredients in the development of the proposed model.