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Empirical Models of Consumer Behavior

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  • Aviv Nevo

Abstract

Models of consumer behavior play a key role in modern empirical Industrial Organization. In this paper, I survey some of the models used in this literature. In particular, I discuss two commonly used demand systems: multi-stage budgeting approaches and discrete choice models. I motivate their use and highlight some key modeling assumptions. I next briefly discuss key issues of estimation, and conclude by summarizing some extensions.

Suggested Citation

  • Aviv Nevo, 2010. "Empirical Models of Consumer Behavior," NBER Working Papers 16511, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:16511
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    Cited by:

    1. Pablo Fajgelbaum & Gene M. Grossman & Elhanan Helpman, 2011. "Income Distribution, Product Quality, and International Trade," Journal of Political Economy, University of Chicago Press, vol. 119(4), pages 721-765.
    2. De Zhou & Xiaohua Yu & Thomas Herzfeld, 2015. "Dynamic food demand in urban China," China Agricultural Economic Review, Emerald Group Publishing, vol. 7(1), pages 27-44, February.
    3. Wiktor L. Adamowicz & Klaus Glenk & Jürgen Meyerhoff, 2014. "Choice modelling research in environmental and resource economics," Chapters,in: Handbook of Choice Modelling, chapter 27, pages 661-674 Edward Elgar Publishing.
    4. Kidokoro, Yukihiro, 2016. "A micro foundation for discrete choice models with multiple categories of goods," Journal of choice modelling, Elsevier, vol. 19(C), pages 54-72.
    5. Lázár Ede, 2014. "Quantifying the Economic Value of Warranties: A Survey," Acta Universitatis Sapientiae, Economics and Business, De Gruyter Open, vol. 2(1), pages 75-94, October.
    6. Evgeny Yakovlev, 2016. "Demand for Alcohol Consumption and Implication for Mortality: Evidence from Russia," Working Papers w0221, Center for Economic and Financial Research (CEFIR).
    7. Federico Ciliberto & GianCarlo Moschini & Edward D. Perry, 2017. "Valuing Product Innovation: Genetically Engineered Varieties in U.S. Corn and Soybeans," Center for Agricultural and Rural Development (CARD) Publications 17-wp576, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    8. Andrew Chesher & Adam M. Rosen, 2014. "An instrumental variable random‐coefficients model for binary outcomes," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 1-19, June.
    9. Leonardo Becchetti & Francesco Salustri & Pasquale Scaramozzino, 2018. "Nudging and Environmental Corporate Responsibility: A Natural Experiment," CEIS Research Paper 426, Tor Vergata University, CEIS, revised 03 Apr 2018.
    10. Dardanoni, V.; & Laudicella, M.; & Li Donni, P.;, 2018. "Hospital Choice in the NHS," Health, Econometrics and Data Group (HEDG) Working Papers 18/04, HEDG, c/o Department of Economics, University of York.
    11. ABE Naohito & INAKURA Noriko & TONOGI Akiyuki, 2016. "Estimation of Aggregate Demand and Supply Shocks Using Commodity Transaction Data," Discussion papers 16040, Research Institute of Economy, Trade and Industry (RIETI).
    12. Jorge Talero Bernal, 2016. "Una comparación del gasto por tres niveles de ingreso para Colombia bajo una estimación del sistema de ecuaciones de demanda Working y Leser y del Sistema Lineal de Gasto Extendido 2008," REVISTA CIFE, UNIVERSIDAD SANTO TOMÁS, March.
    13. Margaret M. Cigno & Elena S. Patel & Edward S. Pearsall, 2013. "Estimates of US postal price elasticities of demand derived from a random- coefficients discrete-choice normal model," Chapters,in: Reforming the Postal Sector in the Face of Electronic Competition, chapter 6, pages 76-88 Edward Elgar Publishing.
    14. Yukihiro Kidokoro, 2015. "Discrete choice models for multicategory goods," GRIPS Discussion Papers 15-08, National Graduate Institute for Policy Studies.
    15. Mendis, Sachintha & Hovhannisyan, Vardges, 2017. "Assessing Provincial-Level Demand For Food Quantity And Quality In China: An Easi Demand System Approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252797, Southern Agricultural Economics Association.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • L0 - Industrial Organization - - General

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