Demand Growth in Services: A Discrete Choice Analysis of Customer Preferences and Online Selling

The selling of perishable services (e.g., hotel rooms, airline seats, and rental cars) online is increasingly popular with both retailers and consumers. Among the innovative approaches to online sales is opaque selling. First popularized by Priceline.com's name‐your‐own‐price model, opaque selling hides some attributes of the service (notably, brand and specific location) until after the purchase decision, in exchange for a discounted price. This means that a branded “product” is being sold as somewhat of a commodity, but the brand “name” is protected by the opaque model. The attraction of this model for retailers is that they are presumably able to increase their revenue stream, albeit at a lower rate, by selling rooms that otherwise would remain in inventory. In this article, we outline the development and analysis of an online choice survey to understand consumer preferences among three types of online distribution channels: regular full information sales channels, and opaque sales channels with or without consumer bidding. A Multinomial Logit model is employed to analyze the data and measure the consumer trade‐offs between price and other attributes of the product. We use the estimated model to calculate the incremental demand and revenue created by using an opaque channel simultaneously with regular full information channels. On balance, we find that correctly priced opaque channels can add to hotels revenue streams without undue cannibalization of regular room sales.     

缺失数据下的逆概率多重加权分位回归估计及其应用

数据缺失问题普遍存在于应用研究中。在随机缺失机制假定下,本文从模型推断角度出发,针对线性缺失分位回归模型,提出一种新的有效估计方法——逆概率多重加权（IPMW）估计。该方法是在逆概率加权（IPW）估计的基础上,结合倾向得分匹配及模型平均思想,经过多次估计,加权确定最终参数估计结果。该方法适用于响应变量是独立同分布或独立非同分布的情形,并适用于绝大多数缺失场景。经过理论推导及模拟研究发现,IPMW估计量在继承IPW估计量的优势上具有更稳健的性质。最后,将该方法应用于含有缺失数据的微观调查数据中,研究了经济较发达的准一线城市中等收入群体消费水平的影响因素,对比两种估计方法的估计结果及置信带,发现逆概率多重加权估计量的标准偏差更小,估计结果更稳健。     

Logistic方法在财务困境预测中的应用

财务困境预测已成为国内外广泛关注的研究领域.财务困境预测方法主要包括多元线性判别法、逻辑回归法等参数方法以及神经网络等非参数方法,本文对各主要方法的优劣进行了评价,阐述了逻辑回归模型在财务困境预测领域的应用现状,并应用Logistic模型对我国上市公司进行了分类预测,在T-2年上取得了较好的预测精度.     

Stock Evaluation Using a Preference Disaggregation Methodology

This paper presents a real application of a multicriteria decision aid (MCDA) approach to portfolio selection based on preference disaggregation, using ordinal regression and linear programming (UTADIS method; UTilités Additives DIScriminantes). The additive utility functions that are derived through this approach have the extrapolation ability that any new alternative (share) can be easily evaluated and classified into one of several user-predefined groups. The procedure is illustrated with a case study of 98 stocks from the Athens stock exchange, using 15 criteria. The results are encouraging, indicating that the proposed methodology could be used as a tool for the analysis of the portfolio managers' preferences and choices. Furthermore, the comparison with multiple discriminant analysis (either using a stepwise procedure or not) illustrates the superiority of the proposed methodology over a well-known multivariate statistical technique that has been extensively used to study financial decision-making problems.     

Utility‐consistent Brand Demand Systems with Endogenous Category Consumption: Principles and Marketing Applications*

This paper introduces the design and implementation of utility‐consistent, brand, and category demand systems. It extends formal demand analysis to the area of brand and category demand, which directly concerns marketing researchers and managers. The proposed brand demand system is a set of interrelated demand functions that are derived explicitly from a utility function describing consumer preferences. The model generalizes by the integration of category expenditures, which are determined endogenously. The theoretical plausibility of the proposed demand model is demonstrated first and, subsequently, brand and category level systems are derived. Econometric methods for estimating the systems are also developed and illustrated in empirical data. The results yield empirically determined, quantitative insights into the structure of consumer demand for brands and product categories. The proposed approach has the attractive feature of structuring the interdependencies of consumer decisions and ensuring an explicit role for theory in applied research.