Before revision:  In many cases the data generating processes used in these Monte Carlo studies do not resemble real data sets and instead reflect many arbitrary decisions made by the researchers. As a result potential users of the methods are rarely persuaded by these simulations that the new methods are as attractive as the simulations make them out to be. We discuss the use of Wasserstein Generative Adversarial Networks ( WGANs) as a method for systematically generating artificial data that mimic closely any given real data set without the researcher having many degrees of freedom .
After revision:  In many cases the data generating processes used in these Monte Carlo studies . The credibility of such Monte Carlo studies is often limited because of the freedom the researcher has in choosing the design. In recent years a new class of generative models emerged in the machine learning literature, termed Generative Adversarial Networks ( WGANs) as a method for systematically generating artificial data that mimic closely any given real data set without the researcher having many degrees of freedom .