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Cross Validation Based Model Selection via Generalized Method of Moments

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  • Junpei Komiyama
  • Hajime Shimao

Abstract

Structural estimation is an important methodology in empirical economics, and a large class of structural models are estimated through the generalized method of moments (GMM). Traditionally, selection of structural models has been performed based on model fit upon estimation, which take the entire observed samples. In this paper, we propose a model selection procedure based on cross-validation (CV), which utilizes sample-splitting technique to avoid issues such as over-fitting. While CV is widely used in machine learning communities, we are the first to prove its consistency in model selection in GMM framework. Its empirical property is compared to existing methods by simulations of IV regressions and oligopoly market model. In addition, we propose the way to apply our method to Mathematical Programming of Equilibrium Constraint (MPEC) approach. Finally, we perform our method to online-retail sales data to compare dynamic market model to static model.

Suggested Citation

  • Junpei Komiyama & Hajime Shimao, 2018. "Cross Validation Based Model Selection via Generalized Method of Moments," Papers 1807.06993, arXiv.org.
    • Handle: RePEc:arx:papers:1807.06993
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    References listed on IDEAS

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    Cited by:

    1. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    2. Masahiro Kato & Hikaru Kawarazaki, 2019. "Model Specification Test with Unlabeled Data: Approach from Covariate Shift," Papers 1911.00688, arXiv.org, revised Feb 2020.

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