When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the discretion the researcher has in choosing the Monte Carlo designs reported. To improve the credibility we propose using a class of generative models that has recently been developed in the machine learning literature, termed Generative Adversarial Networks (GANs) which can be used to systematically generate artificial data that closely mimics existing datasets. Thus, in combination with existing real data sets, GANs can be used to limit the degrees of freedom in Monte Carlo study designs for the researcher, making any comparisons more convincing. In addition, if an applied researcher is concerned with the performance of a particular statistical method on a specific data set (beyond its theoretical properties in large samples), she can use such GANs to assess the performance of the proposed method, the coverage rate of confidence intervals or the bias of the estimator, using simulated data which closely resembles the exact setting of interest. To illustrate these methods we apply Wasserstein GANs (WGANs) to the estimation of average treatment effects. In this example, we find that there is not a single estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, systematic simulation studies can be helpful for selecting among competing methods in this situation, and the generated data closely resemble the actual data.

中文翻译：

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使用 Wasserstein 生成对抗网络设计蒙特卡洛模拟

当研究人员开发新的计量经济学方法时，通常的做法是将新方法的性能与蒙特卡罗研究中现有方法的性能进行比较。由于研究人员在选择报告的蒙特卡罗设计时具有自由裁量权，此类蒙特卡罗研究的可信度通常受到限制。为了提高可信度，我们建议使用最近在机器学习文献中开发的一类生成模型，称为生成对抗网络（GAN），它可用于系统地生成密切模仿现有数据集的人工数据。因此，结合现有的真实数据集，GAN 可以用来限制研究人员蒙特卡罗研究设计的自由度，使任何比较都更有说服力。此外，如果应用研究人员关心特定统计方法在特定数据集上的性能（超出其在大样本中的理论属性），她可以使用此类 GAN 来评估所提出方法的性能、覆盖率置信区间或估计器的偏差，使用与感兴趣的确切设置非常相似的模拟数据。为了说明这些方法，我们应用 Wasserstein GAN (WGAN) 来估计平均治疗效果。在这个例子中，我们发现没有一个估计器在所有三种设置中都优于其他估计器，因此研究人员应该根据给定的设置调整他们的分析方法，系统模拟研究有助于在这种情况下选择竞争方法，并且生成的数据与实际数据非常相似。