Collaborative Research: Deep Inference - Artificial Intelligence for Structural Estimation

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In order to evaluate the effect of economic policies such as extended health care coverage or changes in the minimum wage, economists use structural models which powerfully describe the mechanism at work, but estimating structural models is typically challenging. A main tool for estimating such structural models is inference via simulation. For different parametrizations of the model, synthetic data is generated via the model, and the parameters generating data that most closely resembles observed data are used as estimates. Recent modern artificial intelligence methods such as deep learning for image recognition are based on this same principle. These methods have been achieving impressive results over the past years. Therefore, this research takes advantage of such powerful tools in modern pattern recognition for structural estimation in economics. This research considers a set-up where individual outcomes are a known function of exogenous variables and an error whose distribution is known up to a finite dimensional vector of parameters. The goal is to estimate the finite dimensional parameter. The investigators adopt the generative adversarial network approach (GANs) to find the parameter value such that given a discriminator, a device that can accurately distinguish data generated using the model from real data, is unable to do so when the data is generated according to such parameter value. The method developed in this research differs from other simulation-based minimum distance estimators in that the distance is adaptive. That is, the discriminator learns the features of the data that are best at distinguishing real from synthetic data as opposed to hard-coding what features of the data to match. This adaptability property has proven powerful in pattern recognition tasks. In structural estimation, adaptability can translate into alleviating the curse of dimensionality, and obtaining parameters that are able to more closely match entire distributions of data, as opposed to a set of pre-specified moments. This estimation framework should be useful in applications were distributional effects and heterogeneity are first order to evaluate the effect of a particular policy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.