Best Webinar Awards, II (Chamberlain)

Now let's do the wonderful and pioneering Chamberlain Seminar.

The winner is:

Elena Manresa (NYU), 

for 

"An Adversarial Approach to Structural Estimation,"

with Tetsuya Kaji and Guillaume Pouliot! Check out the paper and video+slides.

It knocked me off my feet (and a few others – there were 900+ viewers). The way I see it -- although the approach is actually much more sophisticated than the description I'm about to give -- she proposes and explores, theoretically and empirically, the use of machine learning (ML) approximators like neural nets (NNs), random forests, etc. as windows for indirect inference in structural econometric models. This is a big deal, as ML approximators are potentially very sophisticated tools for characterizing model and data properties, thereby sharpening our ability to detect divergences between them. Of course her paper raises many questions as well, as does all good research, for example whether the numerous local optima associated with NNs will complicate the resulting indirect inference estimation. In any event the work is tremendously stimulating – a long and exciting way from casual GMM based on a few moments selected in ad hoc fashion, and a very nice bridge between the econometrics and data science / ML literatures. The paper was a real “eureka moment” for me. 

Abstract:

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly’s saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.