Estimation Under Profit-Driven Loss Functions
研究在需求模型中估计价格敏感度参数的问题，当制造商以利润最大化为目标时，传统平方误差损失不再适用，转而采用不对称损失函数，并通过实例表明该方法得到更小的价格敏感度估计值，从而建议更高的最优价格。
Abstract Consider the problem of estimating a price-sensitivity parameter in a demand model. Depending on the context in which the estimate will be used, traditional squared-error loss may be inappropriate. We consider the situation in which the estimate will be used by a manufacturer to set the price. Effectively, the manufacturer's goal of profit maximization induces a loss function that turns out to be asymmetric. Estimators that perform well with respect to such loss functions are obtained. A real example is considered in which, compared to traditional estimation under squared-error loss, this approach leads to smaller price-sensitivity estimates, suggesting higher optimal prices. KEY WORDS: Bayes estimatorDemand modelElasticityMarketingScanner data