Download or read book Incorporating Search and Sales Information in Demand Estimation written by Ali Hortaçsu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Incorporating Sales and Arrivals Information in Demand Estimation written by Ali Hortaçsu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a demand estimation method 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 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 as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

Download or read book Flexible Demand Estimation with Search Data written by Tomomichi Amano and published by . This book was released on 2022 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional methods for estimating demand are not always well-suited to online markets, where individual products are sold infrequently, unobserved factors such as webpage layout drive substitution, and often only a limited set of product characteristics is observed. We propose a demand model where browsing data -- which is abundant in many online settings -- is used to infer individual consumers' consideration sets. In our model, the underlying variables which drive consideration can be correlated arbitrarily across products. We estimate the model through a constraint maximization approach, based on the insight that these correlations should rationalize the product-pair co-search frequencies that are observed in the data. In turn, these correlations make it possible to estimate more flexible substitution patterns. We apply the model to data from an online retailer, recover the elasticity matrix, and solve for optimal prices.

Download or read book Large scale Demand Estimation with Search Data written by Tomomichi Amano and published by . This book was released on 2018 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many online markets are characterized by sellers that stock large numbers of products and sell each product infrequently. At the same time, consumer browsing information is typically tracked by online retailers and is much more abundant than purchase data. We propose a demand model that caters to this type of setting. Our approach, which is based on search and purchase data, is computationally light and allows for flexible substitution patterns. We apply the model to a data set containing browsing and purchase information from a retailer stocking over 500 products, recover the elasticity matrix, and solve for optimal prices for the entire assortment.

Download or read book Demand Estimation with Text and Image Data written by Giovanni Compiani and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit model with product-pair specific nesting parameters that depend on the image and text similarities between products. Our framework does not require collecting product attributes for each category and can capture product similarity along dimensions that are hard to account for with observed attributes. We apply our method to a dataset describing the behavior of Amazon shoppers across several categories and show that incorporating texts and images in demand estimation helps us recover a flexible cross-price elasticity matrix.

Download or read book Econometric Models For Industrial Organization written by Matthew Shum and published by World Scientific. This book was released on 2016-12-14 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Demand Estimation with Infrequent Purchases and Small Market Sizes written by Ali Hortaçsu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a demand estimation method 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 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 as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

Download or read book Prior Information in Demand System Estimation written by Nathan Carl Young and published by . This book was released on 1987 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Demand Estimation Under Stock out Based Substitution from Sales transaction Data written by Tian Lei and published by . This book was released on 2011 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Non and Semi parametric Methods and Applications written by Peter Rossi and published by Princeton University Press. This book was released on 2014-04-27 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.

Download or read book Contributions to Demand Estimation written by and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 1 proposes a moment inequality approach to estimating random utility models when consumers consideration sets are unobservable to econometricians. I show that, without relying on a specific model of consideration set formation, the random utility model can be identified and estimated via a system of conditional moment inequalities derived from the utility maximization assumption. I apply the moment inequality approach to study whether attention inertia can explain some of the observed persistence in consumers brand choices, as opposed to alternative explanations in terms of preference, e.g., state-dependent utilities. The estimation results, obtained using household scanner data, show that up to twenty percent of the observed persistence, in terms of re-purchase probability, can be attributed to the fact that previous purchase of a brand increases its present consideration probability. Chapter 2 introduces a new approach to estimating differentiated product demand system that allows for error in market shares as measures of choice probabilities. In particular, our approach allows for products with zero sales in the data, which is a frequent phenomenon that arises in product differentiated markets but lies outside the scope of existing demand estimation techniques. We use our approach to study consumer demand from scanner data using the Dominicks Finer Foods database, and find that even for the baseline logit model, demand elasticities nearly double when the full error in market shares is taken into account.

Download or read book A Framework for Incorporating Social Media Data in Demand Forecasting for Operational Planning written by Amarnath Chadive and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demand forecasting, the process of predicting future demand for a firm's products, is a crucial element of business success. A key challenge in demand forecasting is the tendency of predictions to become less accurate over longer time horizons, limiting their usefulness for medium- and long-term planning. In recent years, the use of social media data from sources such as Tweets, Google Trends and Amazon reviews has shown promise for improving forecasting accuracy; however, little research exists regarding how long in advance social media data contribute to accuracy. This dissertation uses historical sales data and relevant Twitter and Google search data for a consumer products company to determine the time lag at which social media data improved forecasting accuracy. My results confirmed that social media data improved sales forecasts over the base model at a time lag of 0 and 1 months, but not 2; in particular, lag 0 social media data improved the forecast accuracy for online sales. Results also showed that Twitter sentiment, which indicates customers' attitude toward a product, was more strongly correlated with product sales than Google Trends, which indicate only interest. Based on these results, I propose a framework for use of social media data in operations planning. This research can thus provide business analysts with practical guidance in using social media data to improve their forecasting. It also helps to fill the need for empirical research focused on lag time in use of social media data in forecasting.

Download or read book Revenue Management and Survival Analysis in the Automobile Industry written by André Jerenz and published by Springer Science & Business Media. This book was released on 2008-08-01 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: André Jerenz develops a price-based revenue management framework to support retailers in establishing better and more profitable pricing strategies, including assigning an initial asking price and the adjustment of price over time.

Download or read book Handbook of Industrial Organization written by Kate Ho and published by Elsevier. This book was released on 2021-12-09 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Industrial Organization Volume 4 highlights new advances in the field, with this new volume presenting interesting chapters. Each chapter is written by an international board of authors. Part of the renowned Handbooks in Economics series Chapters are contributed by some of the leading experts in their fields A source, reference and teaching supplement for industrial organizations or industrial economists

Download or read book Demand Estimation with Machine Learning and Model Combination written by Patrick L. Bajari and published by . This book was released on 2015 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. We derive novel asymptotic properties for several of these models. To improve out-of-sample prediction accuracy and obtain parametric rates of convergence, we propose a method of combining the underlying models via linear regression. Our method has several appealing features: it is robust to a large number of potentially-collinear regressors; it scales easily to very large data sets; the machine learning methods combine model selection and estimation; and the method can flexibly approximate arbitrary non-linear functions, even when the set of regressors is high dimensional and we also allow for fixed effects. We illustrate our method using a standard scanner panel data set to estimate promotional lift and find that our estimates are considerably more accurate in out of sample predictions of demand than some commonly used alternatives. While demand estimation is our motivating application, these methods are likely to be useful in other microeconometric problems.

Download or read book DEMAND ESTIMATION WITH HETEROGENOUS CONSUMERS AND UNOBSERVED PRODUCT CHARACTERISTICS A HEDONIC APPROACH written by Patrick BAJARI and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Challenges and Opportunities in Industrial and Mechanical Engineering A Progressive Research Outlook written by S M Pandey and published by CRC Press. This book was released on 2024-06-24 with total page 1036 pages. Available in PDF, EPUB and Kindle. Book excerpt: Present time Industry 4.0 is the need of all industries because it connects industries to AI, high productivity, safety, and flexibility, ensures the 100% utilization of resources across diverse manufacturing systems, and could accelerate normal manufacturing systems to advanced manufacturing systems by using robotics, additive manufacturing, and many more. In this book, the collection of selected papers is constituted from the International Conference on Progressive Research in Industrial & Mechanical Engineering (PRIME 2021), which was at the National Institute of Technology (NIT), Patna, India from August 5 to 7, 2021. This conference brings together all academic people, industry experts, and researchers from India as well as abroad for involving thoughts on the needs, challenges, new technology, opportunities threats in the current transformational field of aspire. This book deliberates on several elements and their relevance to hard-core areas of industrial and mechanical engineering including design engineering, production engineering, indus trial engineering, automobile engineering, thermal and fluid engineering, mechatronics control robotics, interdisciplinary, and many new emerging topics that keep potential in several areas of applications. This book focuses on providing versatile knowledge of cut ting-edge practices to all readers, helping to develop a clear vision toward Industry 4.0, robotics automation, and additive manufacturing in this demanding and evolving time. The book will be a treasured reference for students, researchers, and professionals inter ested in mechanical engineering and allied fields.