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Filling Out the Instrument Set in Mixed Logit Demand Systems for Aggregate Data

Author

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  • Charles J. Romeo

    (Economic Analysis Group, Antitrust Division, U.S. Department of Justice)

Abstract

The random parameters logit model for aggregate data introduced by Berry, Levinsohn, and Pakes (1995) has been a driving force in empirical industrial organization for more than a decade. While these models are identified in theory, identification problems often occur in practice. In this paper we introduce the means of included demographics as a new set of readily available instruments that have the potential to substantially improve numerical performance in a variety of contexts. We use a set of endogenous price simulations to demonstrate that they are valid, and we use a real data illustration to demonstrate that they improve the numerical properties of the GMM objective function. In addition, we develop a metric that decomposes the explanatory power of the model into the proportion of market share variation that is explained by mean utility and that which is explained by the heterogeneity specification.

Suggested Citation

  • Charles J. Romeo, 2010. "Filling Out the Instrument Set in Mixed Logit Demand Systems for Aggregate Data," EAG Discussions Papers 201003, Department of Justice, Antitrust Division.
    • Handle: RePEc:doj:eagpap:201003
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    Citations

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    Cited by:

    1. Abe Dunn & Adam Hale Shapiro, 2011. "Physician Market Power and Medical-Care Expenditures," BEA Working Papers 0078, Bureau of Economic Analysis.
    2. Joachim Freyberger, 2012. "Asymptotic theory for differentiated products demand models with many markets," CeMMAP working papers 19/12, Institute for Fiscal Studies.
    3. Abe Dunn, 2012. "Drug Innovations and Welfare Measures Computed from Market Demand: The Case of Anti-cholesterol Drugs," American Economic Journal: Applied Economics, American Economic Association, vol. 4(3), pages 167-189, July.
    4. Joachim Freyberger, 2012. "Asymptotic theory for differentiated products demand models with many markets," CeMMAP working papers CWP19/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Dutz Mark A. & Orszag Jonathan M. & Willig Robert D., 2012. "The Liftoff of Consumer Benefits from the Broadband Revolution," Review of Network Economics, De Gruyter, vol. 11(4), pages 1-34, December.
    6. Amit Gandhi & Jean-François Houde, 2019. "Measuring Substitution Patterns in Differentiated-Products Industries," NBER Working Papers 26375, National Bureau of Economic Research, Inc.
    7. Ken Heyer & Carl Shapiro, 2010. "The Year in Review: Economics at the Antitrust Division, 2009–2010," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 37(4), pages 291-307, December.

    More about this item

    Keywords

    random coefficients; instrumental variables; identification; GMM; Beer;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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