Bayesian Estimation of Random-Coefficients Choice Models Using Aggregate Data PDF Download
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Author: Andres Musalem
Publisher:
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Languages : en
Pages : 0
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Book Description
This article discusses the use of Bayesian methods for estimating logit demand models using aggregate data, i.e. information solely on how many consumers chose each product. We analyze two different demand systems: independent samples and consumer panel. Under the first system, there is a different and independent random sample of N consumers in each period and each consumer makes only a single purchase decision. Under the second system, the same N consumers make a purchase decision in each of T periods. The proposed methods are illustrated using simulated and real data, and managerial insights available via data augmentation are discussed in detail.
Author: Andres Musalem
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This article discusses the use of Bayesian methods for estimating logit demand models using aggregate data, i.e. information solely on how many consumers chose each product. We analyze two different demand systems: independent samples and consumer panel. Under the first system, there is a different and independent random sample of N consumers in each period and each consumer makes only a single purchase decision. Under the second system, the same N consumers make a purchase decision in each of T periods. The proposed methods are illustrated using simulated and real data, and managerial insights available via data augmentation are discussed in detail.
Author: Renna Jiang
Publisher:
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Category :
Languages : en
Pages : 0
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Book Description
We present a Bayesian approach for analyzing aggregate level sales data in a market with differentiated products. We consider the aggregate share model proposed by Berry, Levinsohn and Pakes (1995) which introduces a common demand shock into an aggregated random coefficient logit model. A full likelihood approach is possible with a specification of the distribution of the common demand shock. We introduce a re-parameterization of the covariance matrix to improve the performance of the random walk Metropolis for covariance parameters. We illustrate the usefulness of our approach with both actual and simulated data. Sampling experiments show that our approach performs well relative to the GMM estimator even in the presence of a mis-specified shock distribution. We view our approach as useful for those who willing to trade off one additional distributional assumption for increased efficiency in estimation.
Author: German Zenetti
Publisher:
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Category :
Languages : en
Pages : 0
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Book Description
The random coefficients logit model is a workhorse in marketing and empirical industrial organizations research. When only aggregate data are available, it is customary to calibrate the model based on market shares as data input, even if the data are available in the form of aggregate counts. However, market shares are functionally related to model primitives in the random coefficients model whereas finite aggregate counts are only probabilistic functions of these model primitives. A recent paper by Park & Gupta (2009) stresses this distinction but is hamstrung by numerical problems when demonstrating its potential practical importance. We develop Bayesian inference for the likelihood function proposed by Park & Gupta, sidestepping the numerical problem encountered by these authors. We show how taking account of the amount of information about shares by modeling counts directly results in improved inference.
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: Siddhartha Chib
Publisher: Emerald Group Publishing
ISBN: 1848553099
Category : Business & Economics
Languages : en
Pages : 656
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Book Description
Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
Author: Andrés Ignacio Musalem
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
Author: John Geweke
Publisher: Oxford University Press
ISBN: 0191618268
Category : Business & Economics
Languages : en
Pages : 576
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Book Description
Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.
Author: Hairong Song
Publisher:
ISBN:
Category :
Languages : en
Pages : 268
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Book Description
Author: Eric Gautier
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author:
Publisher: North Holland
ISBN: 0444637591
Category : Business & Economics
Languages : en
Pages : 632
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Book Description
Handbook of the Economics of Marketing, Volume One: Marketing and Economics mixes empirical work in industrial organization with quantitative marketing tools, presenting tactics that help researchers tackle problems with a balance of intuition and skepticism. It offers critical perspectives on theoretical work within economics, delivering a comprehensive, critical, up-to-date, and accessible review of the field that has always been missing. This literature summary of research at the intersection of economics and marketing is written by, and for, economists, and the book's authors share a belief in analytical and integrated approaches to marketing, emphasizing data-driven, result-oriented, pragmatic strategies. Helps academic and non-academic economists understand recent, rapid changes in the economics of marketing Designed for economists already convinced of the benefits of applying economics tools to marketing Written for those who wish to become quickly acquainted with the integration of marketing and economics
