The Post Double LASSO for Efficiency Analysis

TL;DR

This paper introduces the post double LASSO method for efficiency analysis, leveraging machine learning to improve estimation of inefficiency and frontier primitives in big data contexts, with an application demonstrating its advantages.

Contribution

It develops a novel post double LASSO approach with Neyman orthogonal moments for efficiency analysis, addressing challenges posed by high-dimensional data.

Findings

Enhanced estimation of inefficiency using machine learning.

Derivation of Neyman orthogonal moment conditions for the problem.

Application showing improved performance over existing methods.

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.