Links

Demand Estimation

Demand models are models of what consumers choose, and what those choices imply about preferences, substitution patterns, welfare, and counterfactual prices.

Textbooks, Surveys & Handbooks

Books

  • Ben-Akiva & Lerman (1985) Discrete Choice Analysis: Theory and Application to Travel Demand [link]
  • Anderson et al. (1992) Discrete Choice Theory of Product Differentiation [link]
  • Rossi et al. (2005) Bayesian Statistics and Marketing [link]
  • Discrete Choice Methods with Simulation by Kenneth Train (2009)
  • Hortaçsu & Joo (2023) Structural Econometric Modeling in Industrial Organization and Quantitative Marketing [link]

Handbook Chapters & Review Articles

  • McFadden (1984) Chapter 24 - Econometric analysis of qualitative response models [link]
  • Rossi & Allenby (2003) Bayesian Statistics and Marketing [link]
  • Ackerberg et al. (2007) Chapter 63 - Econometric Tools for Analyzing Market Outcomes [link]
  • Reiss & Wolak (2007) Chapter 64 - Structural Econometric Modeling: Rationales and Examples from Industrial Organization [link]
  • Chandukala et al. (2007) Choice Models in Marketing: Economic Assumptions, Challenges and Trends [link]
  • Chintagunta & Nair (2011) Discrete Choice Models of Consumer Demand in Marketing [link]
  • Nevo (2011) Empirical Models of Consumer Behavior [link]
  • Dubé (2019) Chapter 1 - Microeconometric models of consumer demand [link]
  • Berry & Haile (2021) Foundations of Demand Estimation [link]
  • Gandhi & Nevo (2021) Chapter 2 - Empirical models of demand and supply in differentiated products industries [link]

Foundations

Random Utility & Early Discrete Choice Models

  • Lancaster (1966) A New Approach to Consumer Theory [link]
  • McFadden (1973) Conditional Logit Analysis of Qualitative Choice Behavior [link]
  • Manski (1977) The Structure of Random Utility Models [link]
  • McFadden (1978) Modelling the Choice of Residential Location [link]
  • Hausman & Wise (1978) A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences [link]
  • Guadagni & Little (1983) A Logit Model of Brand Choice Calibrated on Scanner Data [link]
  • Dubin & McFadden (1984) An Econometric Analysis of Residential Electric Appliance Holdings and Consumption [link]
  • Hanemann (1984) Discrete/Continuous Models of Consumer Demand [link]

Simulation & Computation of Choice Probabilities

  • McFadden (1989) A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration [link]
  • Pakes & Pollard (1989) Simulation and the Asymptotics of Optimization Estimators [link]
  • Hajivassiliou et al. (1996) Simulation of multivariate normal rectangle probabilities and their derivatives [link]
  • Brownstone & Train (1998) Forecasting new product penetration with flexible substitution patterns [link]

Individual-Level Data

Heterogeneity & Bayesian Methods

  • Kamakura & Russell (1989) A Probabilistic Choice Model for Market Segmentation and Elasticity Structure [link]
  • Chintagunta et al. (1991) Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data [link]
  • Rossi & Allenby (1993) A Bayesian Approach to Estimating Household Parameters [link]
  • McCulloch & Rossi (1994) An Exact Likelihood Analysis of the Multinomial Probit Model [link]
  • Jain et al. (1994) A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data [link]
  • Rossi et al. (1996) The Value of Purchase History Data in Target Marketing [link]
  • Allenby & Rossi (1998) Marketing Models of Consumer Heterogeneity [link]
  • Revelt & Train (1998) Mixed Logit with Repeated Choices: Households’ Choices of Appliance Efficiency Level [link]
  • McFadden & Train (2000) Mixed MNL models for discrete response [link]

State Dependence, Inertia & Endogeneity

  • Heckman (1981) Heterogeneity and State Dependence [link]
  • Keane (1997) Modeling Heterogeneity and State Dependence in Consumer Choice Behavior — Journal of Business & Economic Statistics 15(3), 310–327
  • Villas-Boas & Winer (1999) Endogeneity in Brand Choice Models [link]
  • Dubé et al. (2009) Do Switching Costs Make Markets Less Competitive? [link]
  • Dubé et al. (2010) State Dependence and Alternative Explanations for Consumer Inertia [link]
  • Petrin & Train (2010) A Control Function Approach to Endogeneity in Consumer Choice Models [link]

Aggregate Data: Differentiated Products

The BLP Framework

  • Bresnahan (1987) Competition and Collusion in the American Automobile Industry: The 1955 Price War [link]
  • Berry (1994) Estimating Discrete-Choice Models of Product Differentiation [link]
  • Berry et al. (1995) Automobile Prices in Market Equilibrium [link]
  • Goldberg (1995) Product Differentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry [link]
  • Feenstra & Levinsohn (1995) Estimating Markups and Market Conduct with Multidimensional Product Attributes [link]
  • Cardell (1997) Variance Components Structures for the Extreme-Value and Logistic Distributions with Application to Models of Heterogeneity [link]
  • Nevo (2000) A Practitioner’s Guide to Estimation of Random-Coefficients Logit Models of Demand [link]
  • Nevo (2000) Mergers with Differentiated Products: The Case of the Ready-to-Eat Cereal Industry [link]
  • Nevo (2001) Measuring Market Power in the Ready-to-Eat Cereal Industry [link]
  • Berry et al. (2004) Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market [link]

Computation, Inference & Software

  • Berry et al. (2004) Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems [link]
  • Jiang et al. (2009) Bayesian Analysis of Random Coefficient Logit Models Using Aggregate Data [link]
  • Dubé et al. (2012) Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation [link]
  • Knittel & Metaxoglou (2014) Estimation of Random-Coefficient Demand Models: Two Empiricists’ Perspective [link]
  • Salanié & Wolak (2019) Fast, “Robust”, and Approximately Correct: Estimating Mixed Demand Systems [link]
  • Conlon & Gortmaker (2020) Best practices for differentiated products demand estimation with PyBLP [link]
  • Gandhi et al. (2023) Estimating demand for differentiated products with zeroes in market share data [link]
  • Conlon & Gortmaker (2023) Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP [link]

Identification & Instruments

Nonparametric Identification

  • Berry et al. (2013) Connected Substitutes and Invertibility of Demand [link]
  • Berry & Haile (2014) Identification in Differentiated Products Markets Using Market Level Data [link]
  • Compiani (2022) Market counterfactuals and the specification of multiproduct demand: A nonparametric approach [link]
  • Berry & Haile (2024) Nonparametric Identification of Differentiated Products Demand Using Micro Data [link]

Instruments & Identifying Variation

  • Hausman et al. (1994) Competitive Analysis with Differentiated Products [link]
  • Nevo & Rosen (2012) Identification with Imperfect Instruments [link]
  • Reynaert & Verboven (2014) Improving the performance of random coefficients demand models: The role of optimal instruments [link]
  • Armstrong (2016) Large Market Asymptotics for Differentiated Product Demand Estimators with Economic Models of Supply [link]
  • Gandhi & Houde (2019) Measuring Substitution Patterns in Differentiated-Products Industries [link]

Beyond the Standard Discrete Choice Model

Continuous & Flexible Demand Systems

  • Christensen et al. (1975) Transcendental Logarithmic Utility Functions [link]
  • Deaton & Muellbauer (1980) An Almost Ideal Demand System [link]
  • Bajari & Benkard (2005) Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach [link]
  • Berry & Pakes (2007) The Pure Characteristics Demand Model [link]

Purchase Incidence, Quantity & Assortment

  • Gupta (1988) Impact of Sales Promotions on When, What, and How Much to Buy [link]
  • Chintagunta (1993) Investigating Purchase Incidence, Brand Choice and Purchase Quantity Decisions of Households [link]
  • Fader & Hardie (1996) Modeling Consumer Choice Among SKUs [link]

Multiple Discreteness & Complementarity

  • Hendel (1999) Estimating Multiple-Discrete Choice Models: An Application to Computerization Returns [link]
  • Dubé (2004) Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks [link]
  • Gentzkow (2007) Valuing New Goods in a Model with Complementarity: Online Newspapers [link]

Consideration Sets & Limited Information

  • Siddarth et al. (1995) Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data [link]
  • Goeree (2008) Limited Information and Advertising in the U.S. Personal Computer Industry [link]
  • Honka et al. (2017) Advertising, consumer awareness, and choice: Evidence from the U.S. banking industry [link]
  • Abaluck & Adams-Prassl (2021) What Do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses [link]

Dynamics

Storable & Durable Goods

  • Rust (1987) Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher [link]
  • Song & Chintagunta (2003) A micromodel of new product adoption with heterogeneous and forward-looking consumers: Application to the digital camera category [link]
  • Hendel & Nevo (2006) Measuring the Implications of Sales and Consumer Inventory Behavior [link]
  • Gowrisankaran & Rysman (2012) Dynamics of Consumer Demand for New Durable Goods [link]
  • Hendel & Nevo (2013) Intertemporal Price Discrimination in Storable Goods Markets [link]
  • Melnikov (2013) Demand for Differentiated Durable Products: The Case of the U.S. Computer Printer Market [link]
  • Ching & Osborne (2020) Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling [link]

Learning & Price Uncertainty

  • Erdem & Keane (1996) Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets [link]
  • Ackerberg (2003) Advertising, learning, and consumer choice in experience good markets: An empirical examination [link]
  • Erdem et al. (2003) Brand and Quantity Choice Dynamics Under Price Uncertainty [link]
  • Crawford & Shum (2005) Uncertainty and Learning in Pharmaceutical Demand [link]
  • Erdem et al. (2008) A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality [link]
  • Ching et al. (2013) Learning Models: An Assessment of Progress, Challenges, and New Developments [link]

Welfare, Valuation & Willingness to Pay

Welfare & New Goods

  • Small & Rosen (1981) Applied Welfare Economics with Discrete Choice Models [link]
  • Hausman (1996) Valuation of New Goods under Perfect and Imperfect Competition [link]
  • Bresnahan (1997) Comment on Hausman’s Valuation of New Goods [link]
  • Petrin (2002) Quantifying the Benefits of New Products: The Case of the Minivan [link]

Willingness to Pay from Choice Data

  • Allenby et al. (2005) Adjusting Choice Models to Better Predict Market Behavior [link]
  • Iyengar et al. (2008) A Conjoint Approach to Multipart Pricing [link]
  • Allenby et al. (2014) Economic Valuation of Product Features [link]
  • Pachali et al. (2022) Omitted Budget Constraint Bias and Implications for Competitive Pricing [link]
  • He et al. (2024) Measuring Willingness to Pay: A Comparative Method of Valuation [link]

Stated Preference & Conjoint Analysis

Reviews & Foundations

  • Green & Srinivasan (1990) Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice [link]
  • Green et al. (2001) Thirty Years of Conjoint Analysis: Reflections and Prospects [link]
  • Agarwal et al. (2015) An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research [link]
  • Allenby et al. (2019) Chapter 3 - Economic foundations of conjoint analysis [link]
  • Ben-Akiva et al. (2019) Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis [link]

Experimental Design & Response Format

  • Louviere & Woodworth (1983) Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data [link]
  • Swait & Louviere (1993) The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models [link]
  • Dhar (1997) Consumer Preference for a No-Choice Option [link]
  • Toubia et al. (2003) Fast Polyhedral Adaptive Conjoint Estimation [link]
  • Brazell et al. (2006) The No-Choice Option and Dual Response Choice Designs [link]
  • Ding (2007) An Incentive-Aligned Mechanism for Conjoint Analysis [link]
  • Liu et al. (2009) Studying the level-effect in conjoint analysis: An application of efficient experimental designs for hyper-parameter estimation [link]
  • Campbell & Erdem (2019) Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider [link]

Estimation with Conjoint Data

  • Allenby et al. (1995) Incorporating Prior Knowledge into the Analysis of Conjoint Studies [link]
  • Marshall & Bradlow (2002) A Unified Approach to Conjoint Analysis Models [link]
  • Fiebig et al. (2010) The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity [link]
  • Train (2016) Mixed logit with a flexible mixing distribution [link]
  • Pachali et al. (2020) How to generalize from a hierarchical model? [link]

Conjoint Beyond Marketing

  • Bridges et al. (2011) Conjoint Analysis Applications in Health—a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force [link]
  • Hainmueller et al. (2014) Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments [link]
  • Hauber et al. (2016) Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force [link]
  • Leeper et al. (2020) Measuring Subgroup Preferences in Conjoint Experiments [link]