International Series in Quantitative Marketing Series Editor Jehoshua Eliashberg The Wharton School, University of Pennsylvania, Philadelphia, PA, USA More information about this series at http://www.springer.com/series/6164 Editors Peter S H Leeflang, Jaap E Wieringa, Tammo H A Bijmolt and Koen H Pauwels Advanced Methods for Modeling Markets Editors Peter S H Leeflang Department of Marketing, University of Groningen, Groningen, The Netherlands Aston Business School, Birmingham, UK Jaap E Wieringa Department of Marketing, University of Groningen, Groningen, The Netherlands Tammo H A Bijmolt Department of Marketing, University of Groningen, Groningen, The Netherlands Koen H Pauwels Department of Marketing, D’Amore-McKim School of Business, Northeastern University, Boston, USA BI Norwegian Business School, Oslo, Norway ISSN 0923-6716 e-ISSN 2199-1057 International Series in Quantitative Marketing ISBN 978-3-319-53467-1 e-ISBN 978-3-319-53469-5 https://doi.org/10.1007/978-3-319-53469-5 Library of Congress Control Number: 2017944728 © Springer International Publishing AG 2017 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface In 2015, we published our book Modeling Markets (MM) In MM, we provide the basics of modeling markets along with the classical steps of the model building process: specification, data collection, estimation, validation, and implementation We spend much attention to models of the aggregate demand, the individual demand, and we give examples of database marketing models The table of contents and the subject index of MM can be found at the end of this volume However, in MM, we did not cover a number of advanced methods that are used to specify, estimate, and validate marketing models Such methods are covered in the present volume: Advanced Methods for Modeling Markets (AMMM) MM is particularly suitable for students in courses such as “models in marketing” and “quantitative analysis in marketing” at the graduate and advanced undergraduate level AMMM is directed toward participants of Ph.D courses and researchers in the marketing science discipline In AMMM, we consider—after an introduction (Part I)—the following topics: Models for advanced analysis (Part II): (Advanced) individual demand models Time series analysis State space models Spatial models Structural models Mediation Models that specify competition Diffusion models In addition, we present models with latent variables, including the estimation methods that they require (Part III): Specification and estimation models with latent variables: Structural equation models Partial least squares Mixture models Hidden Markov models In the part that deals with specific estimation methods and issues, we discuss (Part IV): Generalized methods of moments Bayesian analysis Non-/semi-parametric estimation Endogeneity issues In the final two chapters of this book (Part V), we give an outlook to the future of modeling markets, where we spend explicit attention to machine learning models and big data Each chapter of AMMM contains the following elements: An introduction to the method/methodology A numerical example/application in marketing References to other marketing applications Suggestions about software We, as editors, would like to thank the 24 authors (affiliated to universities in eight different countries) who contributed to this book Our colleague in Groningen Peter C Verhoef came up with the idea to make this book an edited volume and invite authors to contribute their expertise We thank him for this great idea We thank the authors for their contributions and cooperation The four editors contributed to chapters but also reviewed the chapters in cooperation with a number of other reviewers, namely Keyvan Dehmamy, Maarten Gijsenberg, Hans Risselada, and Tom Wansbeek, who are all affiliated to the University of Groningen, the Netherlands We owe much to Linda Grondsma and Jasper Hidding who helped us tremendously in getting the chapters organized Peter S H Leeflang Jaap E Wieringa Tammo H A Bijmolt Koen H Pauwels Groningen, The Netherlands, Groningen, The Netherlands, Groningen, The Netherlands, Boston, USA June 2017 Contents Part I Introduction Advanced Methods for Modeling Markets (AMMM) Peter S H Leeflang, Jaap E Wieringa, Tammo H A Bijmolt and Koen H Pauwels Part II Specification Advanced Individual Demand Models Dennis Fok Traditional Time-Series Models Koen H Pauwels Modern (Multiple) Time Series Models: The Dynamic System Koen H Pauwels State Space Models Ernst C Osinga Spatial Models J Paul Elhorst Structural Models Paulo Albuquerque and Bart J Bronnenberg Mediation Analysis: Inferring Causal Processes in Marketing from Experiments Rik Pieters Modeling Competitive Responsiveness and Game Theoretic Models Peter S H Leeflang 10 Diffusion and Adoption Models Peter S H Leeflang and Jaap E Wieringa Part III Modeling with Latent Variables 11 Structural Equation Modeling Hans Baumgartner and Bert Weijters 12 Partial Least Squares Path Modeling Jörg Henseler 13 Mixture Models Jeroen K Vermunt and Leo J Paas 14 Hidden Markov Models in Marketing Oded Netzer, Peter Ebbes and Tammo H A Bijmolt Part IV Estimation Issues 15 Generalized Method of Moments Tom J Wansbeek 16 Bayesian Analysis Elea McDonnell Feit, Fred M Feinberg and Peter J Lenk 17 Non- and Semiparametric Regression Models Harald J Van Heerde 18 Addressing Endogeneity in Marketing Models Dominik Papies, Peter Ebbes and Harald J Van Heerde Part V Expected Developments 19 Machine Learning and Big Data Raoul V Kübler, Jaap E Wieringa and Koen H Pauwels 20 The Future of Marketing Modeling Koen H Pauwels, Peter S H Leeflang, Tammo H A Bijmolt and Jaap E Wieringa Author Index Subject Index About the Authors Appendix Table of contents from Modeling Markets Subject Index from Modeling Markets Contributors Paulo Albuquerque Faculty of Marketing, INSEAD Fontainebleau, Fontaine-bleau, France Hans Baumgartner Department of Marketing, Smeal College of Business, The Pennsylvania State University, University Park, PA, USA Tammo H A Bijmolt Department of Marketing, Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands Bart J Bronnenberg Department of Marketing, Tilburg School of Economics and Marketing, Tilburg University, Tilburg, The Netherlands Peter Ebbes Department of Marketing, HEC Paris, Jouy-en-Josas, France J Paul Elhorst Department of Economics, Econometrics and Finance, University of Groningen, Groningen, The Netherlands Fred M Feinberg Ross School of Business, University of Michigan, Ann Arbor, MI, USA Elea McDonnell Feit LeBow College of Business, Drexel University, Philadelphia, PA, USA Dennis Fok Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands Harald J Van Heerde Department of Marketing, School of Communication, Journalism and Marketing, Massey University, Auckland, New Zealand Jörg Henseler Department of Design, Production and Management, University of Twente, Enschede, The Netherlands Raoul V Kübler Department of Marketing, Özyeğin University, Istanbul, Turkey Vermunt, J.K (2001) Vermunt, J.K (2003) Vermunt, J.K (2010) Vermunt, J.K and J Magidson (2000–2016) Vermunt, J.K and J Magidson (2002) Vermunt, J.K and J Magidson (2015) Viard, V.B and N Economides (2015) Vijverberg, W.P.M (1997) Vilcassim, N.J., V Kadiyali and P.K Chintagunta (1999) Villas-Boas, J.M (2007) Villas-Boas, J.M and R.S Winer (1999) Villas-Boas, J.M and Y Zhao (2005) Viola, P and M.J Jones (2004) Viswesvaran, C and D.S Ones (2000) Viterbi, A.J (1967) Vitorino, M.A (2012) Vitorino, M.A (2014) Voleti, S., P.K Kopalle and P Ghash (2015) Von Neumann, J and O Morgenstern (1944) Voorhees, C.M., M.K Brady, R Calantone and E Ramirez (2016) Vovsha, P (1997) Vul, E., C Harris, P Winkielman and H Pashler (2009) W Wang, C., R Raina, D Fong, D Zhou, J Han, and G Badros (2011) Wang, L and C Hsiao (2011) Wang, M and D Chan (2011) Wanous, J.P and M.J Hudy (2001) Wansbeek, T.J (2004) Wansbeek, T.J and E Meijer (2000) Wasserman, L (2012) Watson, G.S (1964) Wedel, M and W.A Kamakura (2000) Wedel, M and P Kannan (2016) Wedel, M., R Pieters and J Liechty (2008) Weijters, B., H Baumgartner and N Schillewaert (2013) Weitzman, M.L (1979) Welch, L.R (2003) Wells, W.D (1993) Wen, C.-H and F.S Koppelman (2001) West, M and J Harrison (1997) Wheaton, B., B Muthén, D Alwin and G Summers (1977) White, H.L (1980) Wierenga, B (2008) Wieringa, J.E and C Horváth (2005) Wiesel, T., B Skiera and J Villanueva (2010) Wiesel, T., K.H Pauwels and J Arts (2011) Wildt, A.R (1976) Windmeijer, F (2005) Winer, R.S (1986) Winer, R.S and S.A Neslin (2014) Wittink, D.R., M.J Addona, W.J Hawkes and J.C Porter (2011) Wold, H.O.A (1974) Wold, H.O.A (1982) Wooldridge, J.M (2002) Wooldridge, J.M (2010) Wooldridge, J.M (2012) Wooldridge, J.M (2015) Wright, P.G (1928) Wright, S (1921) Wu, A.D., and B.D Zumbo (2007) Wu, C.H., S.C Kao, Y.Y Su and C.C Wu (2005) Wuyts, S., S Stremersch, C Van den Bulte and P.H Franses (2004) X Xia, G.E., and W.D Jin (2008) Xie, J.X., M Song, M Sirbu and Q Wang (1997) Xue, M., L.M Hitt and P Chen (2011) Y Yamato, J., J Ohya and K Ishii (1992) Yang, S and G.M Allenby (2003) Yang, S., V Narayan and H Assael (2006) Yang, Y., M Shi and A Goldfarb (2009) Yao, S., C.F Mela, J Chiang and Y Chen (2012) Ying, Y., F Feinberg and M Wedel (2006) Yip, G.S (1995) Yoo, S (2003) Yu, J., R de Jong and L Lee (2008) Z Zantedeschi, D., E.M Feit and E.T Bradlow (2016) Zanutto, E.L and E.T Bradlow (2006) Zeithammer, R and P.J Lenk (2006) Zellner, A (1988) Zellner, A and F Palm (1974) Zhang, J and M Wedel (2009) Zhang, J., M Wedel and R Pieters (2009) Zhang, J.Z., O Netzer and A Ansari (2014) Zhang, J.Z., G.F Watson IV., R.W Palmatier and R.P Dant (2016) Zhang, Q., Y Song, Q Liu, S.R Chandukala and P.Z.G Qian (2015) Zhang, T (2015) Zhang, X., S Li, R.R Burke and A Leykin (2014) Zhao, X., J.G Lynch and Q Chen (2010) Zhu, T., V Singh and M.D Manuszak (2009) Ziggers, G.-W and J Henseler (2016) Zivot, E and D.W.K Andrews (1992) Zucchini, W and I.L MacDonald (2009) Subject Index Subject Index (numbers refer to (sub-)sections) Symbols 2SLS estimator, 15.6.3; 18.3.1 A AB -model, 4.4.3 Activation function, 19.5.5 ADANCO, 12.5 Additive Random Utility Model (ARUM), 15.3.6 Adoption model, 10.1; 10.4 Agent-based model, 10.4.3.1 AIC, 4.4.6; 13.2.5; 14.2.7 AIC3, 13.2.5 Akaike’s Information Criterion (AIC) for a VAR with lag p , 4.4.6 Approximaley Normed-fit index (ANO), 11.2.3.1 ARIMA model, 3.2.1 ARMA model, 3.2.4 ARMAX model, 3.3.2 ASSESSOR, 10.4.2 AutoCorrelation Function (ACF), 3.2.1 AutoRegressive Moving Average (ARMA) process, 3.2.4 Average Variance Extracted (AVE), 11.3.1; 12.4.2 B Backpropagation, 19.5.3.6 Backward probability, 14.2.3.2 Badness-of-fit index (BF), 11.2.3.1 Bagging, 19.5.3.2 Bayes estimator, 16.2.2.2 Bayesian analysis, 16 (title) BIC, 4.4.6; Table 11.2; 13.2.5; 14.2.7 Bayesian Information Criterion (BIC) for a VAR of lag order p , 4.4.6 Bayesian MCMC, 16.2.4 Bayesian Structural Equation Modeling (BSEM), 11.3.2 Bayesian theory on model selection, 16.2.5 Bentler-Bonett index, 12.4.1 Bertrand-equilibrium, 9.5.1 Bias-variance tradeoff, 20.3 Big data, 19 (title); 19.2 Big Stats on Small Data, 20.1 Binary logit model, 2.2.1 Binary probit model, 2.2.1 Bollen-Stine bootstrap, 11.4.1 Boosting, 19.5.3.4 Bootstrap, 12.2; 12.4.1; 12.4.3 Burn-in period, 16.2.4.1 Business-as-usual, 4.3.3 C C -model, 4.4.3 CAIC, 14.2.7 CART model, 19.5.3.1 Causal inferences, 8.2.1 Cause-related marketing, 20.5 Censored variables model, 2.5 CHAID model, 19.5.3.1 exhaustive, 19.5.3.1 Churn, 10.3.2.6 Classical demand model, 9.4.1 Classification task, 19.1 Cliff-Ord model, 6.2.1 Cointegration, 3.3.1; 4.3.1 Comparative Fit Index (CFI), Table 11.2 Competitive responsiveness, (title) Competitor-centered assessment, 9.3 Competitor-oriented decision making, 9.4.5 Competitive interaction model, 7.3.3 Competitive reactions advanced, 9.3 multiple, 9.4.2 simple, 9.4.2 Competitive response model, 9.1 Composite model, 12.2 Composite reliability (CR), 11.3.1 Concomitant variable, 13.2.6 Conditional logit model, 2.2.3 (footnote) Conditional probit model, 2.2.3 (footnote) Confirmatory composite analysis, 12.4.2 Confirmatory measurement models, 11.3 Congeneric measurement models, 11.3.1 Congruence, 9.4.5 Conjectural Variation (CV) approach, 9.5.1 Conjugate distribution, 16.2.2.1 Consideration and search, 7.2.3.1 Consistent PLS (PLSc), 12.2 Control function approach, 18.3.2 Corner solutions, 2.5 Cournot-equilibrium, 9.5.1 Covariance-based SEM, 12.1 Cross-correlation function, 3.3.1 Cross-market communication, 10.3.2 Customer-focused assessment, 9.3 Customer-focused decision making, 9.4.5 D Data augmentation, 14.3.4.3 Data model, 16.2.2.1 Data structures, 19.4 Deal effect curve, 17.5.1 Decision making across agents, 7.2.3.3 Decision tree model, 19.5.3 Demand model, 9.4.1; 9.4.4; 9.4.6.1 Demand shock, 7.2.2.2 Differenced series, 3.2.7 Diffusion model, 10 (title); 10.1 Directionality, 8.2.1; 8.3.1 Discrepancy geodesic, 12.4.1 unweighted least squares, 12.4.1 Discrete factor model, 13.2.3 Discriminant validity, 11.3.1 Distance metric, 6.2.1; 19.4.2 Distinctiveness, 8.3.3 Double Asymmetric Structural VAR (DASVAR) model, 4.4.3 Double prewhitening method, 4.2 Drift model, 5.2.2 Dynamic demand model, 7.2.3.2 Dynamic lagged SAR, 6.4.3 Dynamic panel data, 15.6.1.2 Dynamic SAR, 6.4.3 Dynamic spatial panel, 6.4.3 E Effect indicators, 11.2.1 Effect size f , 12.4.3 Efficiency, 18.3.4 (footnote) Endogeneity, 15.6.1.3; 18 (title); 18.2.1; 20.2 Entropy, 19.4.3 Entry model, 7.3.3 Equation measurement, 5.3.1 observation, 5.3.1 state, 5.3.1 transition, 5.3.1 Escalation, 4.3.3 Euclidian distance, 19.4.2 Evolutionary time series, 3.2.2 Evolving business, 4.3.3 Exogeneity strong, 4.4.5 super, 4.4.5 weak, 4.4.5 Expectation-Maximization (EM) algorithm, 13.2.4; 14.3.2 Exploratory Structural Equation Modeling (ESEM), 11.3.2 Explosive time series, 3.2.2 Extended LNB model, 9.4.3; 9.4.4 External influence model, 10.2 Externalities, 10.3.2.5 F Factor loading, 11.2.1 Factor model, 12.2; 12.3 Factor VAR model, 4.4.9 Filtering, 14.2.4.1 Final Prediction Error (FPE), 4.4.6 Finite mixture approach, 14.1 Fixed Effects (FE) model, 18.4 Forecast Error Variance Decomposition (FEVD), 4.6 Formative measurement, 11.3.1 Fornell-Larcker criterion, 12.4.2 Forward probability, 14.2.3.2 Full conditional distribution, 16.2.4.1 Full structural equation models, 11.4 G Game theory, 9.3; 9.5 (empirical) Game-theoretic model, 9.5.5 Gaussian copula, 18.5.3 Gelfand/Dey approximation, 16.2.5 General spatial nesting model (GNS), 6.2.1 Generalized Bass model, 10.3.1 Generalized Extreme Value (GEV) model, 2.3.5 Generalized FEVD (GFEVD), 4.6 Generalized Impulse Response Function (GIRF), 4.5.3 Generalized Method of Moments (GMM),15 (title) theory, 15.4.1 Generalized nested logit, 2.3.6 Generated regressors, 15.3.7 GHK simulator, 2.2.3 Gibbs sampling, 14.3.4.3; 16.2.4.1 Goodness-of-fit-index (GF), 11.2.3.1; 12.4.1 Goodwill models, 5.2.3 Guttman scaling, 13.2.3 H Hannan-Quinn (HQ) criterion for a VAR of lag order p , 4.4.6 Hazard models, 10.4.3.2 Heterogeneity, 2.7.2; 20.2 continuous, 16.4.3 discrete, 16.4.3 HeteroTrait-MonoTrait ratio of correlations (HTMT), 12.4.2 Heywood cases, 12.2 Hidden Markov Model (HMM), 14 (title) non-homogeneous, 14.2.2.3; 14.2.6 semi, 14.2.2.3 Hierarchical Bayes, 16.3.3; 16.4.2 Higher-order spatial process, 6.4.1 Highest Posterior Distribution (HPD) interval, 16.2.2.3 Horizontal competition, 9.5.2 Hurdle model, 2.5.4 Hysteresis, 4.3.3 I I Identification (SEM), 11.2.1; 12.3 Imitation coefficient, 10.2 Imitators, 10.2 Impulse response function, 3.3.1; 4.5.1 Incremental-fit index (IM), 11.2.3.1 Independence of Irrelevant Alternatives (IIA), 2.3.1 Indicators, 11.2.1 Individual demand model, (title) Individual Item Reliability (IIR), 11.3.1 Individual Item Convergent Validity (IICV), 11.3.1 Information gain, 19.4.3 Initial state distribution, 14.2.2.2 Inner model, 12.2 Innovation coefficient, 10.2 Innovators, 10.2 Instrumental variable estimation, 18.3 Instrumental variables, 15.6; 15.6.2; 15.6.3; 15.6.4; 15.7; 18.3.5; 18.3.6 Integrated ARMA model, 3.2.7 Internal influence model, 10.2 Intervention analysis, 3.3.2 Intrafirm activities, 9.4.3 Invariance configural, 11.3.3 metric, 11.3.3 scalar, 11.3.3 Inverse Mills ratio, 2.5.2; 18.6.3 Item Response Theory (IRT), 11.3.4; 13.2.2; 13.2.3 J Joint distribution, 16.2.2.1 Jöreskog’s rho/omega, 12.4.2 K K -model, 4.4.3 K -nearest neighbor estimator, 17.3.1 model, 17.3 Kalman filter, 5.4.2 observation equation, 5.4.2.2 posterior state covariances, 5.4.2.2 posterior state means, 5.4.2.2 prior state covariances, 5.4.2.2 prior state means, 5.4.2.2 state equation, 5.4.2.2 Kalman gain matrix, 5.4.2.1 Kalman smoother, 5.4.3 observation equation, 5.4.3.3 smoother state covariances, 5.4.3.3 smoothed state means, 5.4.3.3 state equation, 5.4.3.3 Kernel estimation, 17.3 estimator, 17.3.2 function, 19.5.2 L L1 norm, 19.4.2 L2 norm, 19.4.2 Label switching, 14.3.4.4 Lag selection in VAR, 4.4.6 Lambda test statistic, 13.2.5 Latent Class (LC), 14.1 analysis, 13.1 approach, 14.2.5 Latent customer preference, 14.1 GOLD, 13.4 Instrumental Variables (LIV) approach, 18.5.2 Markovian process, 14.1 preference state, 14.1 variable, 11.2.1 variable model, 11.1 Layers, 19.5.3.6 Likelihood, 16.2.2.1 Likelihood Ratio (LR) statistic, 4.4.6 Limited dependent variables, 18.6.4 Limited information maximum likelihood, 15.7.3; 18.3.3 Linear Discriminant Analysis (LDA), 19.5.3.1 LISREL, 11.4 LNB model, 9.4.2 Local polynomial regression, 17.3; 17.6 Logit model, 2.2 Lucas critique, 4.7 LVPLS, 12.5 M Machine Learning, 19 (title); 19.1; 19.3 Macro-flow adoption model, 10.4.3 Manhattan distance, 19.4.2 Markov Chain Monte Carlo (MCMC), 16.2.4.1 Markov property, 14.2 Maximum log integrated likelihood, 16.2.5 MCMC, 16.2.4.1 Routines, 16.3 Measurement error, 15.3.4; 15.6.1.4 Measurement model, 11.1; 12.2 Mediation analysis, (title); 8.2 Meta-Bass-model, 10.3.2.2 Method of Moments (MM), 15.2.2 Metropolis-Hastings (MH) algorithm, 14.3.4.2 Minimum fit function chi-square, 11.2.3.1 (table 11.2) Missing data, 16.4.4 Mixed ARMA model, 3.2.4 Mixed-influence model, 10.2 Mixture growth model, 13.1 Mixture model, 2.3.4; 13 (title); 16.4.3 Mixture regression, 13.1 Model selection, 13.2.5; 16.2.5; 18.6.3 Models for long-term performance, 20.1 Models for short-term performance, 20.1 Modification index (MI), 11.2.3.2 Monte Carlo integration, 16.2.4.1 Moving average processes, 3.2.3 Multi equation time series, 4.1 Multi-sample measurement model, 11.3.3 Multi-stage latent variable model, 11.4 Multilevel LC analysis, 13.2.7 Multinomial logit model, 2.2.3 Multinomial probit model, 2.2.3 Multiple endogenous regressors, 18.6.1.1 Multivariate Probit Model (MVP), 16.5.2 Multivariate time series, 3.3 N Naive Bayes, 19.5.4 Nash equilibrium, 9.5.1 Natural Language Processing (NLP), 19.7 Nearest Neighbors (NN classification), 19.4.2 Nested logit model, 2.3.2 Netnography, 20.4 Neural Networks, 19.5.3.6 New Empirical Industrial Organization (NEIO), 9.5.2 Newton/Raftery approximation, 16.2.5 Newton-Raphson (NR) algorithm, 13.2.4 NN-classification, 19.4.2 Nonnormed fit index (NNO), 11.2.3.1 (table 11.2) Nonparametric regression model, 17.1; 17.3 Nonrecursive models, 11.4 Nonstationarity, 3.2.7 Normed-fit index (NO), 11.2.3.1; 12.4.1 (table 11.2) Norton-Bass-model, 10.3.2.2 O Observed variables, 11.2.1 Omitted variables, 8.3.3; 15.6.1.5 Ordered logit, 2.4 Ordered probit, 2.4 Outer model, 12.2 Overfitting, 19.6 P Panel data, 18.4 Parametric model, 17.1 Partial AutoCorrelation Function (PACF), 3.2.1 Partial least squares, 12 (title) Partial hysteresis, 4.5.1 Path data, 20.4 Peak sales, 10.2 Pearson statistic, 13.2.5 Persistence, 3.2.8 Persistent effect, 5.6.1 PLS algorithm, 12.2 path modeling, 12.3 Graph, 12.5 GUI, 12.5 Polynomial forms 3.3.1 Posterior distribution, 16.2.2.1 mean, 16.2.2.1 quantiles, 16.2.2.2 risk, 16.2.2.2 standard deviation, 16.2.2.1 Power condition, 8.3.4 Precision, 16.2.2.1 Prewhitening of variables, 3.3.2 Prior distribution, 16.2.2.1 Probit model, 2.2; 15.3.7 Pruning, 19.5.3.1 Pulse effect, 3.3.2 Q Quadratic regression, 15.3.3 Quasi-Newton algorithm, 5.4.4 QUEST, 19.5.3.1 R Random effects approach, 14.1 Random effects, 18.4.1 Random forest, 19.5.3.3 Random walk, 5.6.1 drift, 5.6.1 model, 5.2.2 Real-time Experience Tracking (RET), 20.4 Recursive models, 11.4 Reduced-Form Vector Autoregressive (RF-VAR) model, 4.4.4 Reflective indicator model, 11.2.1 Reflective measurement model, 11.3.1; 12.2 Repeat-purchase models, 10.4.1 Restricted impulse response function, 4.5.2 Rho Dillon-Goldstein, 12.4.2 Jöreskog, 12.4.2 Root Mean squared Residual (RMR), 11.2.3.1 (table 11.2) Root mean square error correlation, 12.4.1 Root Mean Square Error of Approximation, (RMSEA) 11.2.3.1 S Saddles, 10.3.2 Sample selection model, 2.6 SARAR model, 6.2.1 Satorra-Bentler scaled test statistic, 11.2.2 SCP paradigm, 9.5.2 Seasonal processes, 3.2.9 Semiparametric regression model, 17.1; 17.4 Sign-indeterminacy, 12.3 Simple mixture model, 13.2.1 Sims regression, 4.2 Simulated Maximum Likelihood, 2.2.6 Simultaneity, 15.6.1; 18.3.3 Small Stats on Big Data, 20.1 SmartPLS 3.2, 12.5 Smooth Threshold AutoRegression (STAR) model, 4.4.9 Smoothing, 14.2.4.1 Soft margin machines, 19.5.2 Spatial Durbin error model, 6.2.1 Durbin model, 6.2.1 general spatial nesting model, 6.2.1 lag, 6.2.1 lagged dependent variable, 6.2.1 lagged error term, 6.2.1 lagged explanatory variable, 6.2.1 logit model, 6.4.4 model, (title) panel, 6.4.2 probit model, 6.4.4 Spillover effects, 6.2.3 Spline regression, 17.3.5; 17.7 Squared-error loss, 16.2.2.2 Standardized Root Mean Square Residual (SRMR), 11.2.3.1; 12.4.1 Stackelberg-game, 9.5.1 Stand-alone fit index (SA), 11.2.3.1 State dependence, 14.1; 14.2.2.4 State space model, (title) Linear Gaussian, 5.3 Stationarity mean stationary, 3.2.5 trend stationary, 3.2.5 Stationary process, 3.2.5 time series, 3.2.2 Statistical inference, 20.3 Statistical power, 8.3.4 Step effect, 3.3.2 Stochastic state dependent distribution, 14.1 Stock variable, 5.2.1 Structural breaks, 3.2.5 known, 3.2.6 multiple, 3.2.6 single, 3.2.6 unknown, 3.2.6 Structural demand model, 7.2 equation model, 11 (title); 15.3.5 independence model, 11.4 model, (title); 7.1.1; 11.1; 12.2 supply-side model, 7.3.1 Vector AutoRegressive (SVAR) model, 4.4.3 Structured data, 20.3 Structured Query Language (SQL), 19.7 Subsampling, 19.2 Supervised learning, 19.1; 19.3; 19.5 Support Vector Machines (SVM), 19.5.2 Kernel based, 19.5.2 T Takeoff point, 10.3.2.1 Test Augmented Dickey-Fuller, 3.2.5 cointegration, 4.3.1 Dickey-Fuller, 3.2.5 Full Information Maximum Likelihood (FIML), 4.3.1 Granger causality, 4.2 Hausman, 6.3; 18.3.5.5 KPSS, 3.2.5 likelihood-ratio, 4.4.6; 13.2.5 structural break, 3.2.6 unit root, 3.2.5 Time-series models modern (multiple), (title) traditional, (title) Time-varying competition, 9.5.4 parameter model, 5.2.2 Training data, 19.3 Transfer functions, 3.3.1 Transient effect, 5.6.1 Transition matrix, 14.2.2.3 probability, 14.2.2.3 Treatment effect model, 2.7.1 Trial-repeat model, 10.4.2 True score theory, 12.3 Tucker and Lewis Non-Normed Fit Index (TLI, NNFI), 11.2.3.1 (table 11.2) Two-part model, 2.5.4 Two-stage least squares (2SLS), 18.3.1 Type I – Tobit model, 2.5.2 Type II – Tobit model, 2.6.1; 15.3.7 U Unconfoundedness, 8.2.1; 8.3.2 Unstructured data, 20.3 Unsupervised learning, 19.1; 19.3 V Variables indicators, 11.2.1 observed, 11.2.1 Variance-based SEM, 12.1 Variance inflation factor (VIF), 12.4.2 Vector AutoRegression (VAR) model, 4.4.2 Vector AutoRegressive model with eXogenous variables (VARX), 4.4.4; 9.4.6 Vector AutoRegressive Moving Average (VARMA) model, 4.4.2 Vector Error Correction (VEC), 4.4.7 Vector Moving Average (VMA) model, 4.5.1 Vertical competition, 9.5.3 Video tracking, 20.4 W WarpPLS, 12.5 Weight matrix, 6.2.1 Weighted Least Squares (WLS), 15.4.5 Wold Causal ordering, 4.4.3 X XLSTAT-PLS, 12.5 ... Pauwels (eds.), Advanced Methods for Modeling Markets, International Series in Quantitative Marketing, https://doi.org/10.1007/978-3-319-53469-5_1 Advanced Methods for Modeling Markets (AMMM)... validate marketing models Such methods are covered in the present volume: Advanced Methods for Modeling Markets (AMMM) MM is particularly suitable for students in courses such as “models in marketing”... Contents Part I Introduction Advanced Methods for Modeling Markets (AMMM) Peter S H Leeflang, Jaap E Wieringa, Tammo H A Bijmolt and Koen H Pauwels Part II Specification Advanced Individual Demand