Collaborative Research: Deep Inference - Artificial Intelligence for Structural Estimation

为了评估扩大医保覆盖范围或改变最低工资等经济政策的效果，经济学家使用结构模型，这些模型有力地描述了发挥作用的机制，但估计结构模型通常具有挑战性。估计这种结构模型的一个主要工具是通过模拟进行推理。对于模型的不同参数化，通过模型生成合成数据，并将生成与观测数据最接近的数据的参数用作估计。最近的现代人工智能方法，如用于图像识别的深度学习，都是基于同样的原理。在过去的几年里，这些方法取得了令人印象深刻的成果。因此，本研究利用现代模式识别中的这种强有力的工具来进行经济学中的结构估计。这项研究考虑了这样一种设置，其中个人结果是外部变量的已知函数，而误差的分布直到参数的有限维向量都是已知的。目标是估计有限维参数。研究人员采用生成性对抗性网络方法(GANS)来寻找参数值，以便在给定一个判别器的情况下，当根据该参数值生成数据时，能够准确区分使用该模型生成的数据和真实数据的设备无法这样做。该方法与其他基于仿真的最小距离估计器的不同之处在于距离是自适应的。也就是说，鉴别器学习最善于区分真实数据和合成数据的数据特征，而不是硬编码要匹配的数据特征。这种适应性在模式识别任务中被证明是强大的。在结构估计中，适应性可以转化为缓解维度诅咒，并获得能够更紧密地匹配整个数据分布的参数，而不是一组预先指定的矩。这个评估框架在应用中应该是有用的，因为分布效应和异质性是评估特定政策影响的第一顺序。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.