A Nonparametric Approach to Estimating Heterogeneous Demand from Censored Sales Panel Data PDF Download

Author: Johannes Ferdinand Jörg
Publisher:
ISBN:
Category :
Languages : en
Pages : 29

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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: Chad D. Meyerhoefer
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
In this paper we derive a joint continuous/censored demand system suitable for the analysis of commodity demand relationships using panel data. Unobserved heterogeneity is controlled for using a correlated random effects specification and a Generalized Method of Moments framework used to estimate the model in two stages. While relatively small differences in elasticity estimates are found between a flexible specification and one that restricts the relationship between the random effect and budget shares to be time invariant, larger differences are observed between the most flexible random effects model and a pooled cross sectional estimator. The results suggest the limited ability of such estimators to control for preference heterogeneity and unit value endogeneity leads to parameter bias.

Author: Evgeniy Ozhegov
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Many economic applications, including optimal pricing and inventory management, require predictions of demand based on sales data and the estimation of the reaction of sales to price change. There is a wide range of econometric approaches used to correct biases in the estimates of demand parameters on censored sales data. These approaches can also be applied to various classes of machine learning (ML) models to reduce the prediction error of sales volumes. In this study we construct two ensemble models for demand prediction with and without accounting for demand censorship. Accounting for sales censorship is based on a censored quantile regression where the model estimation was split into two separate parts: a) a prediction of zero sales by the classification model; and b) a prediction of non-zero sales by the regression model. Models with and without censorship are based on the prediction aggregations of least squares, Ridge and Lasso regressions and the Random Forest model. Having estimated the predictive properties of both models, we empirically test the best predictive power of the model taking into account the censored nature of demand. We also show that ML with censorship provides bias corrected estimates of demand sensitivity to price change similar to econometric models.

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: Catalina Stefanescu
Publisher:
ISBN:
Category :
Languages : en
Pages : 33

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Book Description
Demand modeling and forecasting is important for inventory management, retail assortment and revenue management applications. Current practice focuses on univariate demand forecasting, where models are built separately for each product. However, in many industries there is empirical evidence of correlated product demand. In addition, demand is usually observed in several periods during a selling horizon, and it may be truncated due to inventory constraints so that in practice only censored sales data are recorded. Ignoring the inter-product demand correlation or the serial correlation of demand from one selling period to the next leads to biased and inefficient estimates of the true demand distributions. In this paper we propose a class of models for multi-product multiperiod aggregate demand forecasting. We develop an approach for estimating the parameters of the demand models from censored sales data in a maximum likelihood framework using the Expectation-Maximization (EM) algorithm. Through a simulation study, we show that the algorithm is computationally attractive and leads to maximum likelihood estimates with good properties, under different demand and censoring scenarios. We exemplify the methodology with the analysis of two booking data sets from the entertainment and the airline industries, and show that the use of these models in a revenue management setting for airlines increases the revenue by up to 11% relative to the use of alternative demand forecasting methods.

Author: Stefan Hoderlein
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Giovanni Compiani
Publisher:
ISBN:
Category :
Languages : en
Pages : 70

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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: Badi Baltagi
Publisher: John Wiley & Sons
ISBN: 0470518863
Category : Business & Economics
Languages : en
Pages : 239

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Book Description
Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book. The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.

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: Jianqing Fan
Publisher: CRC Press
ISBN: 0429527616
Category : Mathematics
Languages : en
Pages : 942

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Book Description
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.