IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.04945.html
   My bibliography  Save this paper

Estimating Parameters of Structural Models Using Neural Networks

Author

Listed:
  • Yanhao

    (Max)

  • Wei
  • Zhenling Jiang

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 & Wei & Zhenling Jiang, 2025. "Estimating Parameters of Structural Models Using Neural Networks," Papers 2502.04945, arXiv.org.
  • Handle: RePEc:arx:papers:2502.04945
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.04945
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2023. "An Adversarial Approach to Structural Estimation," Econometrica, Econometric Society, vol. 91(6), pages 2041-2063, November.
    2. Youjin Lee & Elizabeth L. Ogburn, 2021. "Network Dependence Can Lead to Spurious Associations and Invalid Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1060-1074, July.
    3. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Harold D. Chiang, 2025. "Maximal Inequalities for Separately Exchangeable Empirical Processes," Papers 2502.11432, arXiv.org, revised Mar 2025.
    2. Brent Simpson & Bradley Montgomery & David Melamed, 2023. "Reputations for treatment of outgroup members can prevent the emergence of political segregation in cooperative networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    4. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.
    5. Marco Letta & Pierluigi Montalbano & Adriana Paolantonio, 2024. "Climate Immobility Traps: A Household-Level Test," Papers 2403.09470, arXiv.org.
    6. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
    7. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
    8. Hui Li & Jian Ni & Fangzhu Yang, 2024. "Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data," Papers 2405.15929, arXiv.org, revised Jun 2024.
    9. Xu, Jian & Zhang, Wei & Li, Hengyun & Zheng, Xiang (Kevin) & Zhang, Jing, 2024. "User-generated photos in hotel demand forecasting," Annals of Tourism Research, Elsevier, vol. 108(C).
    10. Jiang, Zhi-Qiang & Wang, Peng & Ma, Jun-Chao & Zhu, Peican & Han, Zhen & Podobnik, Boris & Stanley, H. Eugene & Zhou, Wei-Xing & Alfaro-Bittner, Karin & Boccaletti, Stefano, 2023. "Unraveling the effects of network, direct and indirect reciprocity in online societies," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    11. Alireza Aghasi & Arun Rai & Yusen Xia, 2024. "A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 616-634, March.
    12. Kübler, Raoul V. & Lobschat, Lara & Welke, Lina & van der Meij, Hugo, 2024. "The effect of review images on review helpfulness: A contingency approach," Journal of Retailing, Elsevier, vol. 100(1), pages 5-23.
    13. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
    14. Irene Botosaru & Isaac Loh & Chris Muris, 2024. "An Adversarial Approach to Identification," Papers 2411.04239, arXiv.org, revised Dec 2024.
    15. Sendhil Mullainathan & Ashesh Rambachan, 2024. "From Predictive Algorithms to Automatic Generation of Anomalies," Papers 2404.10111, arXiv.org.
    16. Ruijie Sun & Feng Liu & Yinan Li & Rongping Wang & Jing Luo, 2024. "Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?," Journal of Business Ethics, Springer, vol. 195(1), pages 151-166, November.
    17. Jean-Jacques Forneron & Zhongjun Qu, 2024. "Fitting Dynamically Misspecified Models: An Optimal Transportation Approach," Papers 2412.20204, arXiv.org.
    18. de Haan, Evert & Padigar, Manjunath & El Kihal, Siham & Kübler, Raoul & Wieringa, Jaap E., 2024. "Unstructured data research in business: Toward a structured approach," Journal of Business Research, Elsevier, vol. 177(C).
    19. Simon J. Blanchard & Theodore J. Noseworthy & Ethan Pancer & Maxwell Poole, 2023. "Extraction of visual information to predict crowdfunding success," Production and Operations Management, Production and Operations Management Society, vol. 32(12), pages 4172-4189, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2502.04945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.