Author: Giovanni Compiani Publisher: ISBN: Category : Languages : en Pages : 70
Book Description
I develop and apply a nonparametric approach to estimate demand in differentiated products markets. Estimating demand flexibly is key to addressing many questions in economics that hinge on the shape - and notably the curvature - of market demand functions. My approach applies to standard discrete choice settings, but accommodates a broader range of consumer behaviors and preferences, including complementarities across goods, consumer inattention, and consumer loss aversion. Further, no distributional assumptions are made on the unobservables and only limited functional form restrictions are imposed. Using California grocery store data, I apply my approach to perform two counterfactual exercises: quantifying the pass-through of a tax, and assessing how much the multi-product nature of sellers contributes to markups. In both cases, I find that estimating demand flexibly has a significant impact on the results relative to a standard random coefficients discrete choice model, and I highlight how the outcomes relate to the estimated shape of the demand functions.
Author: Steven T. Berry Publisher: ISBN: Category : Economics Languages : en Pages : 0
Book Description
We consider nonparametric identification in models of differentiated products markets, using only market level observables. On the demand side we consider a non-parametric random utility model nesting random coefficients discrete choice models widely used in applied work. We allow for product/market-specific unobservables, endogenous product characteristics e.g., prices), and high-dimensional taste shocks with arbitrary correlation and heteroskedasticity. On the supply side we specify marginal costs nonparametrically, allow for unobserved firm heterogeneity, and nest a variety of equilibrium oligopoly models. We pursue two approaches to identification. One relies on instrumental variables conditions used previously to demonstrate identification in a nonparametric regression framework. With this approach we can show identification of the demand side without reference to a particular supply model. Adding the supply side allows identification of firms' marginal costs as well. Our second approach, more closely linked to classical identification arguments for supply and demand models, employs a change of variables approach. This leads to constructive identification results relying on exclusion and support conditions. Our results lead to a testable restriction that provides the first general formalization of Bresnahan's (1982) intuition for empirically discriminating between alternative models of oligopoly competition.
Author: Steven T. Berry Publisher: ISBN: Category : Languages : en Pages : 54
Book Description
We consider nonparametric identification in models of differentiated products markets, using only market level observables. On the demand side we consider a non-parametric random utility model nesting random coefficients discrete choice models widely used in applied work. We allow for product/market-specific unobservables, endogenous product characteristics e.g., prices), and high-dimensional taste shocks with arbitrary correlation and heteroskedasticity. On the supply side we specify marginal costs nonparametrically, allow for unobserved firm heterogeneity, and nest a variety of equilibrium oligopoly models. We pursue two approaches to identification. One relies on instrumental variables conditions used previously to demonstrate identification in a nonparametric regression framework. With this approach we can show identification of the demand side without reference to a particular supply model. Adding the supply side allows identification of firms' marginal costs as well. Our second approach, more closely linked to classical identification arguments for supply and demand models, employs a change of variables approach. This leads to constructive identification results relying on exclusion and support conditions. Our results lead to a testable restriction that provides the first general formalization of Bresnahan's (1982) intuition for empirically discriminating between alternative models of oligopoly competition.
Author: Steven Berry Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
A recent literature considers the identification of heterogeneous demand and supply models via "quasi-experimental'' variation, as from instrumental variables. In this paper we establish nonparametric identification of differentiated products demand when one has "micro data'' linking characteristics of individual consumers to their choices. Micro data provide a panel structure allowing one to exploit variation across consumers within each market, where latent demand shocks are fixed. This facilitates richer demand specifications while substantially softening the reliance on instrumental variables, reducing both the number and types of instruments required. Our results require neither the structure of a "special regressor'' nor a "full support'' assumption on consumer-level observables.
Author: Steven T. Berry Publisher: ISBN: Category : Consumer goods Languages : en Pages : 35
Book Description
Empirical models of demand for-and, often, supply of-differentiated products are widely used in practice, typically employing parametric functional forms and distributions of consumer heterogeneity. We review some recent work studying identification in a broad class of such models. This work shows that parametric functional forms and distributional assumptions are not essential for identification. Rather, identification relies primarily on the standard requirement that instruments be available for the endogenous variables-here, typically, prices and quantities. We discuss the kinds of instruments needed for identification and how the reliance on instruments can be reduced by nonparametric functional form restrictions or better data. We also discuss results on discrimination between alternative models of oligopoly competition.
Author: Patrick Bajari Publisher: ISBN: Category : Consumers' preferences Languages : en Pages : 80
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: Aren Megerdichian Publisher: ISBN: 9781124126838 Category : Breakfast cereals Languages : en Pages : 370
Book Description
This dissertation contains four essays at the intersection of econometrics and industrial organization. In all my chapters, I rely on a detailed set of supermarket scanner data on ready-to-eat cereals. In Chapter 1, I examine identification of price effects for differentiated product markets by relying on a conditional form of exogeneity that is an alternative framework to standard instrumental variables. I simulate price changes in the cereal industry arising from potential mergers between firms, one of which took place in 2008. In Chapter 2, I continue to employ conditional exogeneity to identify the effect of market price on demand for differentiated products. The analysis here departs from past studies of demand in several ways, including relaxing the prevalent assumption that observed product characteristics are exogenous. Estimates of implied price-cost margins based on the conditional exogeneity framework are far more reasonable and stable compared to estimates based on standard instrumental variables procedures. In Chapter 3, we (coauthored with Xun Lu) relax the omnipresent assumption that indirect utility takes a linear-separable parametric form in standard logit models of demand. We rely on conditional independence to structurally identify and nonparametrically estimate the average marginal effect of market price on consumer demand. We find that the effect of price on demand is monotonically increasing in price, resulting in high-priced goods having less elastic own price elasticities, and thus higher implied price-cost margins, which addresses a well-known concern in empirical industrial organization. In Chapter 4, I examine a firm's decision to raise price overtly (by increasing the dollar amount of a good) versus a hidden price change (by decreasing the contents in a good's package). I conduct a comprehensive set of empirical analyses in order to assess the impact of hidden price increases on expenditure share and profitability. During July 2007, General Mills decreased the cereal content for 20 out of 23 of their products in my sample of scanner data. A key finding is that consumers tend to notice hidden price changes on smaller-sized boxes of cereal, leading them to substitute to larger-sized boxes of cereal.
Author: Sandeep Chitla Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In many applications of discrete choice modeling, there exist unobserved factors (UFs) driving the consumer demand that are not included in the model. Ignoring such UFs when fitting the choice model can produce biased parameter estimates and ultimately lead to incorrect policy decisions. At the same time, accounting for UFs during estimation is challenging since we typically have only partial or indirect information about them. Existing approaches such as the classical BLP estimator make strong parametric assumptions to deal with this challenge, and therefore can suffer from model misspecification issues. We propose a novel estimator for dealing with UFs in the mixtures of logit model that is { em nonparametric}, i.e., does not impose any parametric assumptions on the mixing distribution or the underlying mechanism generating the UFs. We theoretically characterize the benefit of using our estimator over the BLP estimator. We then leverage the alternating minimization framework to design an efficient algorithm for implementing our proposed estimator and establish its sublinear convergence to a stationary point of the estimation problem. Using a simulation study, we demonstrate that our estimator is robust to different ground-truth settings, whereas the performance of the BLP estimator suffers significantly under model misspecification. Using real-world grocery sales transaction data, we show that accounting for product and store-level UFs can significantly improve the accuracy of predicting weekly demand at an individual product and store level, with an avg. 57% improvement across 12 product categories over a state-of-the-art benchmark that ignores UFs during estimation.
Author: Johannes Ferdinand Jörg Publisher: ISBN: Category : Languages : en Pages : 29
Book Description
Analyzing historical sales data to draw conclusions on the underlying demand structure is a central foundation for sales planning, e.g. in assortment and revenue optimization. This contribution focuses on estimating the choice behavior of demand segments as well as their distribution from panel data featuring multiple consecutive sales observations. Existing methods in this area mostly utilize parametric models and estimation procedures that rely on some given information, i.e., expert knowledge. To overcome this requirement, we employ finite mixtures to model sales events over multiple time frames and obtain nonparametric demand estimators. The proposed approach requires no given assumptions over underlying distributions. Furthermore, we also introduce a hindsight approach to assign individual sales observations to demand segments. This contribution decreases the need for manual adjustments in demand estimation and allows practitioners to gain detailed insight in purchase behaviors.In an extensive simulation study, we benchmark the approach on different data sets and compare its results to those from published approaches. The study highlights that the approach shows superior performance for markets with heterogeneous demand.
Author: José Luis Moraga-González Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We propose a tractable method for estimation of a simultaneous search model for differentiated products that allows for observed and unobserved heterogeneity in both preferences and search costs. We show that for type I extreme value distributed search costs, expressions for search and purchase probabilities can be obtained in closed form. We show that our search model belongs to the generalized extreme value (GEV) class, which implies that it has a full information discrete-choice equivalent, and hence search data are necessary to distinguish between the search model and the equivalent full information model. We allow for price endogeneity when estimating the model and show how to obtain parameter estimates using a combination of aggregate market share data and individual level data on search and purchases. To deal with the dimensionality problem that typically arises in search models due to a large number of consideration sets we propose a novel Monte Carlo estimator for the search and purchase probabilities. Monte Carlo experiments highlight the importance of allowing for sufficient consumer heterogeneity when doing policy counterfactuals and show that our Monte Carlo estimator is accurate and computationally fast. Finally, a behavioral assumption on how consumers search provides a micro-foundation for consideration probabilities widely used in the literature.
Author: Giovanni Compiani
Author: Steven T. Berry
Author: Steven T. Berry
Author: Steven Berry
Author: Steven T. Berry
Author: Patrick Bajari
Author: Aren Megerdichian
Author: Sandeep Chitla
Author: Johannes Ferdinand Jörg
Author: José Luis Moraga-González