We propose a new statistical instrument-free method to tackle the endogeneity problem. The proposed method models the joint distribution of the endogenous regressor and the error term in the structural equation of interest (the structural error) using a copula method, and it makes inferences on the model parameters by maximizing the likelihood derived from the joint distribution. Similar to the “exclusion restriction” in instrumental variable methods, extant instrument-free methods require the assumption that the unobserved instruments are exogenous, a requirement that is difficult to meet. The proposed method does not require such an assumption. Other benefits of the proposed method are that it allows the modeling of discrete endogenous regressors and offers a new solution to the slope endogeneity problem. In addition to linear models, the method is applicable to the popular random coefficient logit model with either aggregate-level or individual-level data. We demonstrate the performance of the proposed method via a series of simulation studies and an empirical example.

Discrete choice models of demand have typically been estimated assuming that prices are exogenous. Since unobservable (to the researcher) product attributes, such as coupon availability, may impact consumer utility as well as price setting by firms, we treat prices as endogenous. Specifically, prices are assumed to be the equilibrium outcomes of Nash competition among manufacturers and retailers. To empirically validate the assumptions, we estimate logit demand systems jointly with equilibrium pricing equations for two product categories using retail scanner data and cost data on factor prices. In each category, we find statistical evidence of price endogeneity. We also find that the estimates of the price response parameter and the brand-specific constants are generally biased downward when the endogeneity of prices is ignored. Our framework provides explicit estimates of the value created by a brand, i.e., the difference between consumers' willingness to pay for a brand and its cost of production. We develop theoretical propositions about the relationship between value creation and competitive advantage for logit demand systems and use our empirical results to illustrate how firms use alternative value creation strategies to accomplish competitive advantage.Demand Estimation, Logit, Endogeneity, Competitive Strategy

The results indicate that more than 84% of the sales increase due to promotions comes from brand switching. Blattberg and Neslin (1990, pp. 82-83) The general consensus appears to be that brand switching is a major source of volume (due to sales promotions).... Gupta (1988) found that switching accounted for 84% of the increase in coffee brand sales generated by promotion. Putting together the facts that sales promotions generate dramatic immediate sales increases and that brand switching accounts for a large percentage of this increase, we can conclude that sales promotions are strongly associated with brand switching.Wheat and Morrison (1990, p. 167) Gupta (1998) finds that brand switching accounts for most of the sales increase due to promotion, while stockpiling accounts for only 2%.Bucklin and Srinivasan (1991, p. 70)Our approach does not currently incorporate quantity effects of price promotions such as purchase acceleration and stockpiling. (These effects in the coffee category are estimated by Gupta 1988 to be about 16% of the variation in brand volume.)Chiang (1991, p. 309) These results are similar to the ones obtained by Gupta (1998, p. 352), where 84% of the increase is attributed to brand switching, 14% by purchase time acceleration, and 2% by increases in quantity. Karande and Kumar (1995, p. 260) Gupta (1988) showed that 84% of the sales increase due to promotion comes from brand switching. Therefore it is important to study the effect of retailer policies on promotional cross-price elasticities. Gupta et al. (1996, p. 384) The importance of brand choice is underscored by Gupta's (1988) finding that brand switching accounts for 84% of the overall sales increase due to promotions in the coffee category.

The usefulness of a technology product for an end-user often depends on the availability of complementary software products and services. Computers require software, cameras require film, and DVD players require movie programming in order for customers to value the whole product. This phenomenon, where the demand for hardware products is mediated by the supply of complementary software products, is called an network externality. Indirect network externalities create a two-way contingency between the demand for the hardware product and the supply of software products, and result in a strategic interdependence between the actions of hardware manufacturers and the actions of software providers. Indirect network externalities are gaining economic significance in technology markets, because hardware and software are typically provided by independent firms, and both sets of firms have an incentive to free-ride on each others' demand creation efforts. Despite the ubiquity of this phenomenon, it has largely been ignored in the marketing science literature. We present a conceptual and operational model for the evolution of markets with indirect network externalities. The key feature of our framework is to model the between the actions of hardware manufacturers and software complementors, created by the of consumer demand for the whole product on the actions of manufacturers as well as complementors. In addition, we incorporate marketing-mix effects on consumer response, as well as heterogeneity in consumer preferences for hardware and software attributes. We model consumer response using a latent-class choice model. To estimate the complementor response functions, we use a modified Delphi technique that allows us to convert qualitative response data into quantitative response functions. We integrate the consumer and complementor response models to create a simulation model that generates forecasts of market shares and sales volumes for competing technologies, as a function of marketing-mix effects and exogenously specified regulatory scenarios. The modeling framework is of interest to new product modelers interested in creating empirical models and decision-support systems for forecasting demand in technology markets characterized by indirect network externalities. The decision-support aspects of the modeling framework should appeal to managers interested in understanding and quantifying the complex interplay between hardware manufacturers and software complementors in the evolution of markets with indirect network externalities. We present an application of the modeling framework to the U.S. digital television industry, and use the framework to characterize the competition among analog and digital TV technologies. Our results suggest that complementor actions play an important role in the acceptance of digital TV technologies in general, and high definition television (HDTV) in particular. We find that forecasts that ignore the influence of indirect network externalities would be seriously biased in favor of HDTV. We ill...

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W e explore opportunities for targeted pricing for a retailer that only tracks weekly storelevel aggregate sales and marketing-mix information. We show that it is possible, using these data, to recover essential features of the underlying distribution of consumer willingness to pay. Knowledge of this distribution may enable the retailer to generate additional profits from targeting by using choice information at the checkout counter. In estimating demand we incorporate a supply-side model of the distribution channel that captures important features of competitive price-setting behavior of firms. This latter aspect helps us control for the potential endogeneity generated by unmeasured product characteristics in aggregate data. The channel controls for competitive aspects both between manufacturers and between manufacturers and a retailer. Despite this competition, we find that targeted pricing need not generate the prisoner's dilemma in our data. This contrasts with the findings of theoretical models due to the flexibility of the empirical model of demand. The demand system we estimate captures richer forms of product differentiation, both vertical and horizontal, as well as a more flexible distribution of consumer heterogeneity.

In this paper we describe the pass-through behavior of a major U.S. supermarket chain for 78 products across 11 categories. Our data set includes retail prices and wholesale prices for stores in 15 retail price zones for a one-year period. For the empirical model, we use a reduced-form approach that focuses directly on equilibrium prices as a function of exogenous supply- and demand-shifting variables. The reduced-form approach enables us to identify the theoretical pass-through rate without specific assumptions about the form of consumer demand or the conduct of a category-pricing manager. Thus, our measurements of pass-through are not constrained by specific structure on the underlying economic model. The empirical pricing model includes costs of all competing products in the category on the right-hand side (not only the cost of the focal brand) and yields estimates of both own-brand and cross-brand pass-through rates. Our results provide a rich picture of the retailer's pass-through behavior. We find that pass-through varies substantially across products and across categories. Own-brand pass-through rates are, on average, more than 60% for 9 of 11 categories, a finding that is at odds with the claims of manufacturers about retailers in general. Importantly, we find substantial evidence of cross-brand pass-through effects, indicating that retail prices of competing products are adjusted in response to a change in the wholesale price of any given product in the category. We find that cross-brand pass-through rates are both positive and negative. We explore determinants of own-brand and cross-brand pass-through rates and find strong evidence in multiple categories of asymmetric retailer response to trade promotions on large versus small brands. For example, brands with larger market shares, and brands that contribute more to retailer profits in the category, receive higher pass-through. We also find that trade promotions on large brands are less likely than small brands to generate positive cross-brand pass-through, i.e., induce the retailer to reduce the retail price of competing smaller products. On the other hand, small share brands are disadvantaged along three dimensions. Trade promotions on small brands receive low own-brand pass-through generate positive cross-brand pass-through for larger competing brands. Moreover, small share brands do not receive positive cross pass-through from trade promotions on these larger competitors. We also find that store brands are similarly disadvantaged with respect to national brands.pricing, promotion, retailing, channels of distribution, econometric models

The occurrence of temporary stock-outs at retail is common in frequently purchased product categories. Available empirical evidence suggests that when faced with stock-outs, consumers are often willing to buy substitute items. An important implication of this consumer behavior is that observed sales of an item no longer provide a good measure of its core demand rate. Sales of items that stock-out are right-censored, while sales of other items are inflated because of substitutions. Knowledge of the true demand rates and substitution rates is important for the retailer for a variety of category management decisions such as the ideal assortment to carry, how much to stock of each item, and how often to replenish the stock. The estimated substitution rates can also be used to infer patterns of competition between items in the category. In this paper we propose methods to estimate demand rates and substitution rates in such contexts. We develop a model of customer arrivals and choice between goods that explicitly allows for possible product substitution and lost sales when a customer faces a stock-out. The model is developed in the context of retail vending, an industry that accounts for a sizable part of the retail sales of many consumer products. We consider the information set available from two kinds of inventory tracking systems. In the best case scenario of a perpetual inventory system in which times of stock-out occurrence and cumulative sales of all goods up to these times are observed, we derive Maximum Likelihood Estimates (MLEs) of the demand parameters and show that they are especially simple and intuitive. However, state-of-the-art inventory systems in retail vending provide only periodic data, i.e., data in which times of stock-out occurrence are unobserved or “missing.” For these data we show how the Expectation-Maximization (EM) algorithm can be employed to obtain the MLEs of the demand parameters by treating the stock-out times as missing data. We show an application of the model to daily sales and stocking data pooled across multiple beverage vending machines in a midwestern U.S. city. The vending machines in the application carry identical assortments of six brands. Since the number of parameters to be estimated is too large given the available data, we discuss possible restrictions of the consumer choice model to accomplish the estimation. Our results indicate that demand rates estimated naively by using observed sales rates are biased, even for items that have very few occurrences of stock-outs. We also find significant differences among the substitution rates of the six brands. The methods proposed in our paper can be modified to apply to many nonvending retail settings in which consumer choices are observed, not their preferences, and choices are constrained because of unavailability of items in the choice set. One such context is in-store grocery retailing, where similar issues of information availability arise. In this context an important issue that would need to be dealt with is changes in t...