Bayesian Semi-Parametric and Non-Parametric Methods in Marketing and Micro-Econometrics PDF Download
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Author: Peter E. Rossi
Publisher:
ISBN:
Category :
Languages : en
Pages : 214
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Book Description
I review and develop Bayesian non-parametric and semi-parametric methods based on finite and infinite mixtures of normals. Applications include regression, IV methods, and random coefficient models.
Author: Peter E. Rossi
Publisher:
ISBN:
Category :
Languages : en
Pages : 214
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Book Description
I review and develop Bayesian non-parametric and semi-parametric methods based on finite and infinite mixtures of normals. Applications include regression, IV methods, and random coefficient models.
Author: Peter Rossi
Publisher: Princeton University Press
ISBN: 1400850304
Category : Business & Economics
Languages : en
Pages : 219
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Book Description
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.
Author: Peter Rossi
Publisher: Princeton University Press
ISBN: 0691145326
Category : Business & Economics
Languages : en
Pages : 218
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Book Description
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.
Author: Brian Matthew Hartman
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Global marketing managers are keenly interested in being able to predict the sales of their new products. Understanding how a product is adopted over time allows the managers to optimally allocate their resources. With the world becoming ever more global, there are strong and complex interactions between the countries in the world. My work explores how to describe the relationship between those countries and determines the best way to leverage that information to improve the sales predictions. In Chapter II, I describe how diffusion speed has changed over time. The most recent major study on this topic, by Christophe Van den Bulte, investigated new product di ffusions in the United States. Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Additionally, his model contains the implicit assumption that the diffusion speed parameter is constant throughout the life of a product. I model the time component as a nonparametric function, allowing the speed parameter the flexibility to change over time. I find that early in the product's life, the speed parameter is higher than expected. Additionally, as the Internet has grown in popularity, the speed parameter has increased. In Chapter III, I examine whether the interactions can be described through a reference hierarchy in addition to the cross-country word-of-mouth eff ects already in the literature. I also expand the word-of-mouth e ffect by relating the magnitude of the e ffect to the distance between the two countries. The current literature only applies that e ffect equally to the n closest countries (forming a neighbor set). This also leads to an analysis of how to best measure the distance between two countries. I compare four possible distance measures: distance between the population centroids, trade ow, tourism ow, and cultural similarity. Including the reference hierarchy improves the predictions by 30 percent over the current best model. Finally, in Chapter IV, I look more closely at the Bass Diffusion Model. It is prominently used in the marketing literature and is the base of my analysis in Chapter III. All of the current formulations include the implicit assumption that all the regression parameters are equal for each country. One dollar increase in GDP should have more of an eff ect in a poor country than in a rich country. A Dirichlet process prior enables me to cluster the countries by their regression coefficients. Incorporating the distance measures can improve the predictions by 35 percent in some cases.
Author: P.H. Franses
Publisher: Elsevier
ISBN: 0762308575
Category : Business & Economics
Languages : en
Pages : 362
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Book Description
In the 16th Edition of "Advances in Econometrics", we present twelve papers discussing the current interface between Marketing and Econometrics. The authors are leading scholars in the fields and introduce the latest models for analysing marketing data. The papers are representative of the types of problems and methods that are used within the field of marketing. Marketing focuses on the interaction between the firm and the consumer. Economics encompasses this interaction as well as many others. Economics, along with psychology and sociology, provides a theoretical foundation for marketing. Given the applied nature of marketing research, measurement and quantitative issues arise frequently. Quantitative marketing tends to rely heavily upon statistics and econometrics. However, quantitative marketing can place a different emphasis upon the problem than econometrics, even when using the same techniques. A basic difference between quantitative marketing research and econometrics tends to be the pragmatism that is found in many marketing studies. Another important motivating factor in marketing research is the type of data that is available. Applied econometrics tends to rely heavily on data collected by governmental organizations. In contrast, marketing often uses data collected by private firms or marketing research firms. Observational and survey data are quite similar to those used in econometrics. However, the remaining types of data, panel and transactional, can look quite different from what may be familiar to econometricians. The automation and computerization of much of the sales transaction process leaves an audit trail that results in huge quantities of data. A popular area of study is the use of scanner data collected at the checkout stand using bar code readers. Methods that work for small data sets may not work well in these larger data sets. In addition, new sources of data, such as clickstream data from a web site, will offer new challenges. This volume addresses these and related issues.
Author: John Geweke
Publisher: Oxford University Press, USA
ISBN: 0199559082
Category : Business & Economics
Languages : en
Pages : 571
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Book Description
A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.
Author:
Publisher: Elsevier
ISBN: 0444637656
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
Author: Natalie Mizik
Publisher: Edward Elgar Publishing
ISBN: 1784716758
Category : Business & Economics
Languages : en
Pages : 713
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Book Description
Marketing Science contributes significantly to the development and validation of analytical tools with a wide range of applications in business, public policy and litigation support. The Handbook of Marketing Analytics showcases the analytical methods used in marketing and their high-impact real-life applications. Fourteen chapters provide an overview of specific marketing analytic methods in some technical detail and 22 case studies present thorough examples of the use of each method in marketing management, public policy, and litigation support. All contributing authors are recognized authorities in their area of specialty.
Author: Saisandeep Reddy Satyavolu
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
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Book Description
The proliferation of available data in marketing has placed an emphasis on the applicability of extant marketing models to big data. To tackle this problem, methods from machine learning have been increasingly applied by the marketing community. This line of research is a subset of research in marketing that is becoming interdisciplinary. A number of marketing researchers have successfuly adopted methods from other seemingly unrelated fields in their research. In that vein, this thesis examines the applicability of Bayesian Nonparametric methods (from the field of machine learning) to marketing. The first chapter of this thesis provides a very brief survey of marketing research papers that have enhanced pure marketing models using methods from machine learning. The second chapter describes the Dirichlet Process, a key component of Bayesian Nonparametric analysis and provides two synthetic data applications. Going forward, we study the applicability of Bayesian Nonparametric methods to model Heterogeneity across multiple markets. Bayesian Nonparametric methods have been used in marketing and economics literature to model heterogeneity in discrete choice models, but past applications have only been limited to data from a single market. So as to compare heterogeneity in consumer preferences across multiple markets, we use the Hierarchical Dirichlet Process (HDP) which lets multiple "groups" of data "share statistical strength". Heterogeneity across multiple markets is modeled using the HDP in two different contexts (B2C and B2B) in this thesis. Our work shows that the HDP provides a convenient "middle ground" to other extreme modeling options, which are (1) ignore heterogeneity of preferences across markets and (2) model each market separately. Another aspect of the HDP is the ease with which it can be incorporated into models of discrete choice. The models developed and estimated in this thesis are also helpful for the marketing manager. In the B2C application, the results of the model provide the manager with a practical way of tailoring targeting activities towards consumers with varying preferences. Finally, in the B2B application, we find that based on the Stage of the selling process, some marketing activities play a larger role than others in converting sales leads into clients. These results provide a data driven basis for the manager to appropriately allocate marketing dollars to activities based on the selling process.
Author: William A. Barnett
Publisher: Cambridge University Press
ISBN: 9780521424318
Category : Business & Economics
Languages : en
Pages : 512
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Book Description
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
