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An MPEC Estimator for the Sequential Search Model

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  • Shinji Koiso
  • Suguru Otani

Abstract

This paper proposes a constrained maximum likelihood estimator for sequential search models, using the MPEC (Mathematical Programming with Equilibrium Constraints) approach. This method enhances numerical accuracy while avoiding ad hoc components and errors related to equilibrium conditions. Monte Carlo simulations show that the estimator performs better in small samples, with lower bias and root-mean-squared error, though less effectively in large samples. Despite these mixed results, the MPEC approach remains valuable for identifying candidate parameters comparable to the benchmark, without relying on ad hoc look-up tables, as it generates the table through solved equilibrium constraints.

Suggested Citation

  • Shinji Koiso & Suguru Otani, 2024. "An MPEC Estimator for the Sequential Search Model," Papers 2409.04378, arXiv.org.
    • Handle: RePEc:arx:papers:2409.04378
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    References listed on IDEAS

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    1. Jean‐Pierre Dubé & Jeremy T. Fox & Che‐Lin Su, 2012. "Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation," Econometrica, Econometric Society, vol. 80(5), pages 2231-2267, September.
    2. Zhenling Jiang & Tat Chan & Hai Che & Youwei Wang, 2021. "Consumer Search and Purchase: An Empirical Investigation of Retargeting Based on Consumer Online Behaviors," Marketing Science, INFORMS, vol. 40(2), pages 219-240, March.
    3. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
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