AEA Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Adversarial Inference Is Efficient
AEA Papers and Proceedings
vol. 111,
May 2021
(pp. 621–25)
Abstract
We study properties of the adversarial framework, introduced in Kaji, Manresa and Pouliot (2020). We show that the adversarial inference with an oracle classifier is statistically efficient. In addition, we study the finite sample properties of the adversarial estimation framework for the autoregressive parameter of a linear dynamic fixed effects panel data model with Gaussian errors. Unlike maximum likelihood, but similarly as other minimum distance estimators, the adversarial estimators do not suffer from the incidental parameter bias. In our simulations, using a one-hidden-layer neural network as discriminator delivers the estimates with smallest root mean squared error.Citation
Kaji, Tetsuya, Elena Manresa, and Guillaume A. Pouliot. 2021. "Adversarial Inference Is Efficient." AEA Papers and Proceedings, 111: 621–25. DOI: 10.1257/pandp.20211037Additional Materials
JEL Classification
- C51 Model Construction and Estimation
- C38 Classification Methods; Cluster Analysis; Principal Components; Factor Models
- C45 Neural Networks and Related Topics
- C22 Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C23 Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models