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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: 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: 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: 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: Gregory S. Crawford Publisher: ISBN: Category : Consumers' preferences Languages : en Pages : 62
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: Amit Gandhi Publisher: ISBN: Category : Demand (Economic theory) Languages : en Pages : 37
Book Description
Abstract: We develop an estimator for the parameters of a utility function that has interactions between the unobserved demand error and observed factors including price. We show that the Berry (1994)/Berry, Levinsohn, and Pakes (1995) inversion and contraction can still be used to recover the mean utility term that now contains both the demand error and the interactions with the error. However, the instrumental variable (IV) solution is no longer consistent because the price interaction term is correlated with the instrumented price. We show that the standard conditional moment restrictions (CMRs) do not generally suffice for identification. We supplement the standard CMRs with new moments that we call â??generalizedâ?? control function moments and we show together they are sufficient for identification of all of the demand parameters. A major advantage of our setup is that it requires little more than the existence of the same instruments used in this standard IV setting. We run several monte carlos that show our approach works when the standard IV approaches fail because of non-separability. We also test and reject additive separability in the original Berry, Levinsohn, and Pakes (1995) automobile data, and we show that demand becomes significantly more elastic when the correction is applied
Author: Sandeep Chitla
Author: Sandeep Chitla
Author: Jay M. Lillywhite
Author: Giovanni Compiani
Author: Patrick Bajari
Author: Gregory S. Crawford
Author: Ying Li
Author: Stefan Hoderlein
Author: In-Sik Min
Author: Patrick BAJARI
Author: Amit Gandhi