论文标题
对抗性估计的对抗方法
An Adversarial Approach to Structural Estimation
论文作者
论文摘要
我们为结构模型提出了一种新的基于仿真的估计方法，即对抗性估计。估计器被配制为对生成器（使用结构模型生成模拟观测值）和鉴别器（该歧视器（该分类）是否模拟观测值的模拟观测值的最小值问题的解决方案。鉴别器最大化其分类的准确性，而发电机将其最小化。我们表明，借助足够丰富的歧视者，对抗估计器在正确的规范下达到参数效率，并且在错误指定下的参数速率达到了参数效率。我们主张将神经网络用作可以利用适应性属性并达到快速收敛速率的歧视者。我们将我们的方法应用于老年人的储蓄决策模型，并表明我们的估计器将遗产动机揭示为在财富分配中储蓄的重要来源，而不仅仅是富人。
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 simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). 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 our estimator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.
