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Journal ArticleDOI

Discrete/continuous models of consumer demand

01 May 1984-Econometrica (Econometric Society)-Vol. 52, Iss: 3, pp 541-562
About: This article is published in Econometrica.The article was published on 1984-05-01. It has received 786 citations till now. The article focuses on the topics: Demand curve.
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TL;DR: In this article, the authors derive from the Bayesian learning framework how brand choice probabilities depend on past usage experience and advertising exposure, and then form likelihood functions for the models and estimate them using simulated maximum likelihood.
Abstract: We construct two models of the behavior of consumers in an environment where there is uncertainty about brand attributes. In our models, both usage experience and advertising exposure give consumers noisy signals about brand attributes. Consumers use these signals to update their expectations of brand attributes in a Bayesian manner. The two models are 1 a dynamic model with immediate utility maximization, and 2 a dynamic “forward-looking” model in which consumers maximize the expected present value of utility over a planning horizon. Given this theoretical framework, we derive from the Bayesian learning framework how brand choice probabilities depend on past usage experience and advertising exposures. We then form likelihood functions for the models and estimate them on Nielsen scanner data for detergent. We find that the functional forms for experience and advertising effects that we derive from the Bayesian learning framework fit the data very well relative to flexible ad hoc functional forms such as exponential smoothing, and also perform better at out-of-sample prediction. Another finding is that in the context of consumer learning of product attributes, although the forward-looking model fits the data statistically better at conventional significance levels, both models produce similar parameter estimates and policy implications. Our estimates indicate that consumers are risk-averse with respect to variation in brand attributes, which discourages them from buying unfamiliar brands. Using the estimated behavioral models, we perform various scenario evaluations to find how changes in marketing strategy affect brand choice both in the short and long run. A key finding obtained from the policy experiments is that advertising intensity has only weak short run effects, but a strong cumulative effect in the long run. The substantive content of the paper is potentially of interest to academics in marketing, economics and decision sciences, as well as product managers, marketing research managers and analysts interested in studying the effectiveness of marketing mix strategies. Our paper will be of particular interest to those interested in the long run effects of advertising. Note that our estimation strategy requires us to specify explicit behavioral models of consumer choice behavior, derive the implied relationships among choice probabilities, past purchases and marketing mix variables, and then estimate the behavioral parameters of each model. Such an estimation strategy is referred to as “structural” estimation, and econometric models that are based explicitly on the consumer's maximization problem and whose parameters are parameters of the consumers' utility functions or of their constraints are referred to as “structural” models. A key benefit of the structural approach is its potential usefulness for policy evaluation. The parameters of structural models are invariant to policy, that is, they do not change due to a change in the policy. In contrast, the parameters of reduced form brand choice models are, in general, functions of marketing strategy variables e.g., consumer response to price may depend on pricing policy. As a result, the predictions of reduced form models for the outcomes of policy experiments may be unreliable, because in making the prediction one must assume that the model parameters are unaffected by the policy change. Since the agents in our models choose among many alternative brands, their choice probabilities take the form of higher-order integrals. We employ Monte-Carlo methods to approximate these integrals and estimate our models using simulated maximum likelihood. Estimation of the dynamic forward-looking model also requires that a dynamic programming problem be solved in order to form the likelihood function. For this we use a new approximation method based on simulation and interpolation techniques. These estimation techniques may be of interest to researchers and policy makers in many fields where dynamic choice among discrete alternatives is important, such as marketing, decision sciences, labor and health economics, and industrial organization.

1,272 citations

Journal ArticleDOI
TL;DR: The authors applied the standard neoclassical economic framework to generate predictions about how rational agents would answer such survey questions, which in turn implies how such survey data should be interpreted, and compared different survey formats with respect to the information that the question itself reveals to the respondent, the strategic incentives the respondent faces in answering the question, and the information revealed by the respondent's answer.
Abstract: Surveys are frequently used by businesses and governments to elicit information about the public’s preferences. They have become the most common way to gather preference information regarding goods, that are not (or are not yet) bought or sold in markets. In this paper we apply the standard neoclassical economic framework to generate predictions about how rational agents would answer such survey questions, which in turn implies how such survey data should be interpreted. In some situations, the standard economic model would be expected to have no predictive power. For situations where it does have predictive power, we compare different survey formats with respect to: (a) the information that the question itself reveals to the respondent, (b) the strategic incentives the respondent faces in answering the question, and (c) the information revealed by the respondent’s answer.

1,222 citations

Journal ArticleDOI
Koichiro Ito1
TL;DR: In this paper, the authors exploit price variation at spatial discontinuities in electricity service areas, where households in the same city experience substantially different nonlinear pricing and find strong evidence that consumers respond to average price rather than marginal or expected marginal price.
Abstract: Nonlinear pricing and taxation complicate economic decisions by creating multiple marginal prices for the same good. This paper provides a framework to uncover consumers’ perceived price of nonlinear price schedules. I exploit price variation at spatial discontinuities in electricity service areas, where households in the same city experience substantially different nonlinear pricing. Using household-level panel data from administrative records, I find strong evidence that consumers respond to average price rather than marginal or expected marginal price. This suboptimizing behavior makes nonlinear pricing unsuccessful in achieving its policy goal of energy conservation and critically changes the welfare implications of nonlinear pricing. (JEL D12, L11, L94, L98, Q41)

714 citations

Journal ArticleDOI
TL;DR: A review of the state of the art of environmental valuation with discrete choice experiments (DCEs) can be found in this article, where a survey and experimental design, econometric analysis of choice data and welfare analysis are discussed.

667 citations

References
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Journal ArticleDOI
TL;DR: In this article, the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias is discussed, and the asymptotic distribution of the estimator is derived.
Abstract: Sample selection bias as a specification error This paper discusses the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or «omitted variables» bias. A simple consistent two stage estimator is considered that enables analysts to utilize simple regression methods to estimate behavioral functions by least squares methods. The asymptotic distribution of the estimator is derived.

23,995 citations

Journal Article
TL;DR: The problem of translating the theory of economic choice behavior into concrete models suitable for analyzing housing location and methods for controlling the size of data collection and estimation tasks by sampling alternatives from the full set of alternatives are discussed.
Abstract: The problem of translating the theory of economic choice behavior into concrete models suitable for analyzing housing location is discussed. The analysis is based on the premise that the classical, economically rational consumer will choose a residential location by weighing the attributes of each available alternative and by selecting the alternative that maximizes utility. The assumption of independence in the commonly used multinomial logit model of choice is relaxed to permit a structure of perceived similarities among alternatives. In this analysis, choice is described by a multinomial logit model for aggregates of similar alternatives. Also discussed are methods for controlling the size of data collection and estimation tasks by sampling alternatives from the full set of alternatives. /Author/

3,138 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used a subsample of the 1975 survey of 3249 households carried out by the Washington Center for Metropolitan Studies (WCMS) for the Federal Energy Administration for the purpose of testing the statistical exogeneity of appliance dummy variables typically included in demand for electricity equations.
Abstract: Recent micro-simulation studies of the demand for clectricity hy residences have attempted to modlel jointly the demand for appliance and the denmanid for electricity by appliance. Within this context it becomes important to test the statistical exogeneity of appliance dummy variables typically included in demand for electricity equations. If, as the theory would suggest, the demand for durables and their use are related decisions by the consumer, specifications which ignore this fact will lead to biased and inconsistent estimates of price and income elasticities. The present paper attempts to test this bias using a subsample of the 1975 survey of 3249 households carried out by the Washington Center for Metropolitan Studies (WCMS) for the Federal Energy Administration. We discuss and derive a unified model of the demand for consumer durables and the derived demand for electricity. To determine the magnitude of the bias resulting from estimating a unit electricity" consumption (UEC) equation bv ordinary least squares when unobserved factors influence both choice of appliances and intensity of use. we intr-oduce and cstimate a joint water-heat space-heat choice model, and concluide with the consistent estimation and specification of demand for electricity equations.

1,667 citations

Journal ArticleDOI
TL;DR: In this paper, the authors integrate economic concepts of supply and demand equilibrium for urban activities using the concept of traffic equilibrium within transportation networks and describe the cutting edge in travel demand analysis using the latest methods.
Abstract: Describes the cutting edge in travel demand analysis using the latest methods. Emphasizing mathematical modeling techniques, this is the first book to integrate economic concepts of supply and demand equilibrium for urban activities using the concept of traffic equilibrium within transportation networks. Models for optimal transportation are integrated with demand models. Transit travel and goods movement are specifically addressed.

1,601 citations

Book
01 Jan 1971

1,548 citations