The Post Double LASSO for Efficiency Analysis
Econometrics
2025-05-21 v1 Machine Learning
Abstract
Big data and machine learning methods have become commonplace across economic milieus. One area that has not seen as much attention to these important topics yet is efficiency analysis. We show how the availability of big (wide) data can actually make detection of inefficiency more challenging. We then show how machine learning methods can be leveraged to adequately estimate the primitives of the frontier itself as well as inefficiency using the `post double LASSO' by deriving Neyman orthogonal moment conditions for this problem. Finally, an application is presented to illustrate key differences of the post-double LASSO compared to other approaches.
Keywords
Cite
@article{arxiv.2505.14282,
title = {The Post Double LASSO for Efficiency Analysis},
author = {Christopher Parmeter and Artem Prokhorov and Valentin Zelenyuk},
journal= {arXiv preprint arXiv:2505.14282},
year = {2025}
}