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Double machine learning with gradient boosting and its application to the Big N audit quality effect

Author

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  • Yang, Jui-Chung
  • Chuang, Hui-Ching
  • Kuan, Chung-Ming
Abstract
In this paper, we study the double machine learning (DML) approach of Chernozhukov et al. (2018) for estimating average treatment effect and apply this approach to examine the Big N audit quality effect in the accounting literature. This approach relies on machine learning methods and is suitable when a high dimensional nuisance function with many covariates is present in the model. This approach does not suffer from the “regularization bias” when a learning method with a proper convergence rate is used. We demonstrate by simulations that, for the DML approach, the gradient boosting method is fairly robust and to be preferred to other methods, such as regression tree, random forest, support vector regression machine, and the conventional Nadaraya–Watson nonparametric estimator. We then apply the DML approach with gradient boosting to estimate the Big N effect. We find that Big N auditors have a positive effect on audit quality and that this effect is not only statistically significant but also economically important. We further show that, in contrast to the results of propensity score matching, our estimates of said effect are quite robust to the hyper-parameters in the gradient boosting algorithm.

Suggested Citation

  • Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
    • Handle: RePEc:eee:econom:v:216:y:2020:i:1:p:268-283
    DOI: 10.1016/j.jeconom.2020.01.018
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
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    10. Ye Luo & Martin Spindler & Jannis Kuck, 2016. "High-Dimensional $L_2$Boosting: Rate of Convergence," Papers 1602.08927, arXiv.org, revised Jul 2022.
    11. Kothari, S.P. & Leone, Andrew J. & Wasley, Charles E., 2005. "Performance matched discretionary accrual measures," Journal of Accounting and Economics, Elsevier, vol. 39(1), pages 163-197, February.
    12. Connie L. Becker & Mark L. Defond & James Jiambalvo & K.R. Subramanyam, 1998. "The Effect of Audit Quality on Earnings Management," Contemporary Accounting Research, John Wiley & Sons, vol. 15(1), pages 1-24, March.
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    Cited by:

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    More about this item

    Keywords

    Audit quality; Average treatment effect; Big N effect; Double machine learning; Gradient boosting; Performance-matched discretionary accruals;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing

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