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Estimating Parameters of Structural Models Using Neural Networks

Author

Listed:
  • Yanhao (Max) Wei

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Zhenling Jiang

    (Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We study an alternative use of machine learning. We train neural nets to provide the parameter estimate of a given (structural) econometric model, for example, discrete choice or consumer search. Training examples consist of datasets generated by the econometric model under a range of parameter values. The neural net takes the moments of a dataset as input and tries to recognize the parameter value underlying that dataset. Besides the point estimate, the neural net can also output statistical accuracy. This neural net estimator (NNE) tends to limited-information Bayesian posterior as the number of training datasets increases. We apply NNE to a consumer search model. It gives more accurate estimates at lighter computational costs than the prevailing approach. NNE is also robust to redundant moment inputs. In general, NNE offers the most benefits in applications where other estimation approaches require very heavy simulation costs. We provide code at: https://nnehome.github.io .

Suggested Citation

  • Yanhao (Max) Wei & Zhenling Jiang, 2025. "Estimating Parameters of Structural Models Using Neural Networks," Marketing Science, INFORMS, vol. 44(1), pages 102-128, January.
    • Handle: RePEc:inm:ormksc:v:44:y:2025:i:1:p:102-128
    DOI: 10.1287/mksc.2022.0360
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