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Reliable estimation of random coefficient logit demand models

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  • Brunner, Daniel
  • Heiss, Florian
  • Romahn, André
  • Weiser, Constantin

Abstract

The differentiated demand model of Berry, Levinsohn and Pakes (1995) is widely used in empirical economic research. Previous literature has demonstrated numerical instabilities of the corresponding GMM estimator that give a wide range of parameter estimates and economic implications depending on technical details such as the choice of optimization algorithm, starting values, and convergence criteria. We show that these instabilities are mainly driven by numerical approximation errors of the moment function which is not analytically available. With accurate approximation, the estimator is well-behaved. We also discuss approaches to mitigate the computational burden of accurate approximation and provide code for download.

Suggested Citation

  • Brunner, Daniel & Heiss, Florian & Romahn, André & Weiser, Constantin, 2017. "Reliable estimation of random coefficient logit demand models," DICE Discussion Papers 267, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    • Handle: RePEc:zbw:dicedp:267
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    References listed on IDEAS

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    4. Anna Goeddeke & Justus Haucap & Annika Herr & Christian Wey, 2018. "Flexibility in Wage Setting Under the Threat of Relocation," LABOUR, CEIS, vol. 32(1), pages 1-22, March.
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    7. Kandelhardt, Johannes, 2023. "Flexible estimation of random coefficient logit models of differentiated product demand," DICE Discussion Papers 399, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    8. Jean-Jacques Forneron & Serena Ng, 2021. "Estimation and Inference by Stochastic Optimization: Three Examples," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 626-630, May.
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    10. Bartosz Olesiński, 2020. "The Analysis of the Tobacco Product Bans Using a Random Coefficients Logit Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 113-144, June.
    11. Zsolt Sándor, 2019. "Further evidence on sparse grids-based numerical integration in the mixed logit model," Economics Bulletin, AccessEcon, vol. 39(4), pages 2726-2731.
    12. C. Luke Watson & Oren Ziv, 2021. "Is the Rent Too High? Land Ownership and Monopoly Power," CESifo Working Paper Series 8864, CESifo.
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    14. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
    15. Doi, Naoshi, 2022. "A simple method to estimate discrete-type random coefficients logit models," International Journal of Industrial Organization, Elsevier, vol. 81(C).

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