Econometrics of Policy Evaluation
Sunday, Jan. 6, 2019 10:15 AM - 12:15 PM
- Chair: Jeffrey Wooldridge, Michigan State University
Optimal Estimation when Researcher and Social Preferences are Misaligned
AbstractEconometric analysis typically focuses on the statistical properties of fixed estimators and ignores researcher choices. In this article, I approach the analysis of experimental data as a mechanism-design problem that acknowledges that researchers choose between estimators, sometimes based on the data and often according to their own preferences. Specifically, I focus on covariate adjustments, which can increase the precision of a treatment-effect estimate, but open the door to bias when researchers engage in specification searches. First, I establish that unbiasedness is a requirement on the estimation of the average treatment effect that aligns researchers’ preferences with the minimization of the mean-squared error relative to the truth, and that fixing the bias can yield an optimal restriction in a minimax sense. Second, I provide a constructive characterization of all treatment-effect estimators with fixed bias as sample-splitting procedures. Third, I show how these results imply flexible pre-analysis plans for randomized experiments that include beneficial specification searches and offer an opportunity to leverage machine learning.
Deep Inference: AI for Structural Estimation
AbstractWe propose a new estimation method for structural models based on Artificial Intelligence tools. The approach leverages on the availability of modern pattern recognition methods, discriminators, that can accurately distinguish between real data from generated data using a fully specified model and a particular choice of structural parameter values. The estimator is defined as the value of the structural parameters for which the discriminator is unable to distinguish between the true data and the corresponding generated data. Different types of discriminators define different estimators and we show that when using a logit model as a discriminator the estimator is asymptotically equivalent to the popular optimally weighted simulated method of moments (e.g. Gourieroux, Monfort, and Renault (1993)). Discriminators based on Neural Networks with one or multiple hidden layers can provide more efficient estimators. We showcase the good properties of the proposed method using simulated data from a two-period Roy Model where heterogeneous individuals can choose to work between two locations in exchange of wages, and there are returns to experience. Compared to an indirect inference estimator with optimal weighting, our estimator, when using a logit as a discriminator, has smaller standard deviation at no additional computational cost.
- C2 - Single Equation Models; Single Variables