Demand Estimation with Unobserved Choice Set Heterogeneity PDF Download
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Author: Gregory S. Crawford
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 62
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Book Description
We present a method to estimate preferences in the presence of unobserved choice set heterogeneity. We build on the insights of Chamberlain's Fixed-Effect Logit and exploit information in observed purchase decisions in either panel or cross-section environments to construct "sufficient sets" of choices that lie within consumers' true but unobserved choice sets. This allows us to recover preference parameters without having to specify the process of choice set formation. We illustrate our ideas by estimating demand for chocolate bars on-the-go using individual-level data from the UK. Our results show that failing to account for unobserved choice set heterogeneity can lead to statistically and economically significant biases in the estimation of preference parameters.
Author: Gregory S. Crawford
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 62
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Book Description
We present a method to estimate preferences in the presence of unobserved choice set heterogeneity. We build on the insights of Chamberlain's Fixed-Effect Logit and exploit information in observed purchase decisions in either panel or cross-section environments to construct "sufficient sets" of choices that lie within consumers' true but unobserved choice sets. This allows us to recover preference parameters without having to specify the process of choice set formation. We illustrate our ideas by estimating demand for chocolate bars on-the-go using individual-level data from the UK. Our results show that failing to account for unobserved choice set heterogeneity can lead to statistically and economically significant biases in the estimation of preference parameters.
Author: Patrick Bajari
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 80
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Book Description
We study the identification and estimation of preferences in hedonic discrete choice models of demand for differentiated products. In the hedonic discrete choice model, products are represented as a finite dimensional bundle of characteristics, and consumers maximize utility subject to a budget constraint. Our hedonic model also incorporates product characteristics that are observed by consumers but not by the economist. We demonstrate that, unlike the case where all product characteristics are observed, it is not in general possible to uniquely recover consumer preferences from data on a consumer's choices. However, we provide several sets of assumptions under which preferences can be recovered uniquely, that we think may be satisfied in many applications. Our identification and estimation strategy is a two stage approach in the spirit of Rosen (1974). In the first stage, we show under some weak conditions that price data can be used to nonparametrically recover the unobserved product characteristics and the hedonic pricing function. In the second stage, we show under some weak conditions that if the product space is continuous and the functional form of utility is known, then there exists an inversion between a consumer's choices and her preference parameters. If the product space is discrete, we propose a Gibbs sampling algorithm to simulate the population distribution of consumers' taste coefficients.
Author: Alessandro Iaria
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Robert Nathanael Donnelly
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This dissertation consists of three essays related to the analysis of heterogeneity in consumer preferences based on individual level data on historical choices. In particular, they are connected by their application of modern Bayesian approaches to model consumers who differ both in their preferences for observed characteristics as well as their preferences for characteristics that are unobserved by the econometrician, but can instead be inferred from the correlations in choice behavior across different subsets of the population of consumers. The three chapters of this dissertation are also connected by their focus on scalability (both in computation and statistical efficiency) to large choice sets. Large choice sets are all around us, and the rise of E-commerce is leading to even larger sets of products that consumers can choose between. The average grocery store has tens of thousands of unique SKUs. The South Bay region around Stanford University has thousands of restaurants to choose between when you decide to go out for lunch. Large web retailers like Amazon sell hundreds of millions of distinct items. Individual level data on choices in situations like these present both opportunities and challenges. While these data sources are often large and rich in information, it is almost always the case that the number of choice occasions that we observe for any single individual is very small relative to the number of possible items they could have chosen between. Some types of products are easily described as a bundle of characteristics that consumers have preferences over, for example cars (horsepower, number of doors, leather seats) or digital cameras (resolution, zoom, flash), however for many other product categories it is more difficult to find a ''feature representation'' of products that accurately captures the heterogeneity in preferences across consumers. What are the characteristics that differ between Coke and Pepsi that lead to such strong disagreements over which is best. My work builds on recently developed approaches from machine learning for estimating models with large numbers of latent variables. This allows us to infer latent ''characteristics'' of products that are not directly observed by the econometrician, but can be inferred based on similarities in choice patterns across a large set of consumers. This allows us to model consumer preferences with heterogeneity in preferences for both observed and unobserved product characteristics. The first chapter of this dissertation is a paper written together with Susan Athey, David Blei, Francisco Ruiz, and Tobias Schmidt which analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each restaurant has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant opens or closes and compare our predictions to the actual realized outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location. The second chapter is a paper written together with Susan Athey, David Blei, and Francisco Ruiz applies a similar approach in the context of supermarket scanner data. This paper demonstrates a method for estimating consumer preferences among discrete choices, where the consumer makes choices from many different categories. The consumer's utility is additive in the different categories, and her preferences about product attributes as well as her price sensitivity vary across products. Her preferences are correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes, a more realistic functional form for price sensitivity, and products going out of stock. We incorporate the information about the product hierarchy, so that consumers are assumed to select at most one alternative within a category. We evaluate the performance of the model using held-out data from weeks with price changes. We show that our model improves over traditional modeling approaches that consider each category in isolation, when we evaluate the ability of the model to predict responsiveness to price changes (using held-out data from a large number of price changes that occurred in our sample). We show that one source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts. The third chapter of this dissertation proposes a novel estimator for learning heterogeneous consumer preferences based on both browsing and purchase data from online retailers with large product assortments. This work was done in collaboration with Ilya Morozov. Despite increasing availability data on the product pages consumers browse prior to making a purchase, the existing marketing literature provides little guidance on how retailers can use it to make better marketing decisions. In this paper, we propose an empirical framework that allows to efficiently extract information from consumers' search histories and use it to design personalized product recommendations. Our framework is based on the standard consideration set model from the marketing literature. To extract information from the unstructured search data, we augment the model with rich consumer heterogeneity and include several unobserved product characteristics. We then propose a way to estimate this model's parameters using a latent factorization approach from the computer science literature. The proposed framework can be seen as combining a structural approach to modeling consumer consideration from marketing with nonparametric estimation methods commonly used in the computer science. We are in discussion with a large online retailer to gain access to data and to run an AB test to experimentally validate the effects of improved rankings and recommendations of products.
Author: Ali Hortaçsu
Publisher:
ISBN:
Category : Commerce
Languages : en
Pages :
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Book Description
We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data and measures of consumer search intensity. After presenting simulation studies, we consider an empirical application of air travel demand where product-level sales are sparse. We find considerable variation in demand over time. Periods of peak demand feature both larger market sizes and consumers with higher willingness to pay. This amplifies cyclicality. However, observed frequent price and capacity adjustments offset some of this compounding effect.
Author: Matthew Shum
Publisher: World Scientific
ISBN: 981310967X
Category : Business & Economics
Languages : en
Pages : 154
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Book Description
Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.
Author: Lihua Bai
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Accurate estimates of the future demand and the substitution probabilities between products are important inputs for retail assortment optimization. However, these quantities are difficult to estimate as the estimation involves understanding and modeling of consumer's choice behavior when a complete choice set is available as well as when the choice set is incomplete (i.e., their response to out-of-stock situations). In this paper, we propose a prospect theory based reference dependent preference structure for consumer choice and use the information available in store scanner sales and inventory data to estimate both the true demand for the products as well as the substitution probabilities between products. We use a combination of theoretical arguments, simulations, and empirical analysis to establish that the proposed reference dependent logit (RDL) model yields robust and efficient estimates. We also compare the performance of RDL against the multinomial logit, two models that use exogenous substitution probabilities, and experts' opinions regarding what the true substitution probabilities could be.
Author: Zhentong Lu
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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This paper proposes a new approach to estimating multinomial choice models when each consumer's actual choice set is unobservable but could be bounded by two known sets, i.e., the largest and smallest possible choice sets. The bounds on choice set, combined with a monotonicity property derived from utility maximization, imply a system of inequality restrictions on observed choice probabilities that could be used to identify and estimate the model. A key insight is that the identification of random utility model can be achieved without exact information on consumers' choice sets, which generalizes the identification result of the standard multinomial choice model. The effectiveness of the proposed approach is demonstrated via a range of Monte Carlo experiments as well as an empirical application to consumer demand for potato chips using household scanner data.
Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399
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Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: Maura Coughlin
Publisher:
ISBN:
Category :
Languages : en
Pages : 179
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Book Description
This dissertation consists of three essays that evaluate how consumers make decisions in settings where the researcher may not know the set of alternatives from which observed choices were selected. Many empirical analyses in economics presume the researcher knows the full set of alternatives an individual compared when selecting their most preferred. In practice, this assumption may fail to hold for a variety of reasons. In the first chapter, I introduce the economic setting of unobserved choice sets and consideration sets defining to this work. In the second chapter, my coauthors and I propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents' choice sets and their preferences. We first establish that the model is partially identified and characterize its sharp identification region. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. The third chapter evaluates the prescription drug insurance choices of Medicare beneficiaries. I propose an empirical model of demand for prescription drug plans where non-monetary plan attributes stochastically determine the composition of the set of plans that an individual considers, and monetary plan attributes determine the individual's expected utility over contracts in her consideration set. This model reconciles the classic view of insurance contracts as lotteries with purely monetary outcomes with the empirical finding that choice among insurance plans is driven by their non-monetary attributes and financial attributes beyond their impacts on costs. I estimate the model using data from Medicare Part D allowing for unobserved heterogeneity in risk aversion and in consideration sets. I find that the latter plays a crucial role in plan choices, and in contrast to previous literature that assumes full consideration of all plans, I uncover an important role for risk aversion in determining individual choices.
