计量经济分析 = Econometric analysis

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 simulated 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. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

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Discrete Choice Methods with Simulation

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Principles of data mining

Part 1: Introduction. 1. The Historical Development of the Market Segmentation Concept. 2. Segmentation Bases. 3. Segmentation Methods. 4. Tools for Market Segmentation. Part 2: Segmentation Methodology. 5. Clustering Methods. 6. Mixture Models. 7. Mixture Regression Models. 8. Mixture Unfolding Models. 9. Profiling Segments. 10. Dynamic Segmentation. Part 3: Special Topics in Market Segmentation. 11. Joint Segmentation. 12. Market Segmentation with Tailored Interviewing. 13. Model-Based Segmentation Using Structural Equation Models. 14. Segmentation Based on Product Dissimilarity Judgements. Part 4: Applied Market Segmentation. 15. General Observable Bases: Geo-demographics. 16. General Unobservable Bases: Values and Lifestyles. 17. Product-specific observable Bases: Response-based Segmentation. 18. Product-Specific Unobservable Bases: Conjoint Analysis. Part 5: Conclusions and Directions for Future Research. 19. Conclusions: Representations of Heterogeneity. 20. Directions for Future Research. References. Index.

Market Segmentation: Conceptual and Methodological Foundations

The authors examine antecedents of consumer innovativeness in a cross-national context. They propose a framework that distinguishes individual difference variables and national cultural variables. ...

A cross -national investigation into the individual and national cultural antecedents of consumer innovativeness

The three key ad elements (brand, pictorial, and text) each have unique superiority effects on attention to advertisements, which are on par with many commonly held ideas in marketing practice. This is the main conclusion of an analysis of 1363 print advertisements tested with infrared eye-tracking methodology on more than 3600 consumers. The pictorial is superior in capturing attention, independent of its size. The text element best captures attention in direct proportion to its surface size. The brand element most effectively transfers attention to the other elements. Only increments in the text element’s surface size produce a net gain in attention to the advertisement as a whole. The authors discuss how their findings can be used to render more effective decisions in advertising.

https://www.jstor.org/stable/info/30161988

Attention Capture and Transfer in Advertising: Brand, Pictorial, and Text-Size Effects:

The authors provide a critical examination of marketing analytics methods by tracing their historical development, examining their applications to structured and unstructured data generated within or external to a firm, and reviewing their potential to support marketing decisions. The authors identify directions for new analytical research methods, addressing (1) analytics for optimizing marketing-mix spending in a data-rich environment, (2) analytics for personalization, and (3) analytics in the context of customers’ privacy and data security. They review the implications for organizations that intend to implement big data analytics. Finally, turning to the future, the authors identify trends that will shape marketing analytics as a discipline as well as marketing analytics education.

Marketing Analytics for Data-Rich Environments

Advertising needs to capture consumers' attention in likable ways, and the visual complexity of advertising plays a central role in this regard. Yet ideas about visual complexity effects conflict, and objective measures of complexity are rare. The authors distinguish two types of visual complexity, differentiate them from the difficulty of comprehending advertising, and propose objective measures for each. Advertisements are visually complex when they contain dense perceptual features (“feature complexity”) and/or when they have an elaborate creative design (“design complexity”). An analysis of 249 advertisements that were tested with eye-tracking shows that, as the authors hypothesize, feature complexity hurts attention to the brand and attitude toward the ad, whereas design complexity helps attention to both the pictorial and the advertisement as a whole, its comprehensibility, and attitude toward the ad. This is important because design complexity is under direct control of the advertiser. The...

Michel Wedel

Papers

Market Segmentation: Conceptual and Methodological Foundations

A cross -national investigation into the individual and national cultural antecedents of consumer innovativeness

Attention Capture and Transfer in Advertising: Brand, Pictorial, and Text-Size Effects:

Marketing Analytics for Data-Rich Environments

The stopping power of advertising: measures and effects of visual complexity