Panel Stochastic Frontier Models with Latent Group Structures

TL;DR

This paper introduces a flexible estimation framework for panel stochastic frontier models that captures heterogeneity via latent groups, improving efficiency analysis in sectors like banking.

Contribution

It develops a novel hybrid estimation method tailored for stochastic frontier models with latent group structures, applicable to various fixed effects specifications.

Findings

Simulation studies show strong finite-sample performance.

Empirical application to U.S. banking sector demonstrates practical utility.

Abstract

Stochastic frontier models have attracted considerable attention due to the incorporation of an inefficiency term in addition to the conventional error term. In this paper, we propose a general estimation framework for panel stochastic frontier models that accommodates potential heterogeneity through latent group structures. The framework is tailored to the distinctive features of stochastic frontier models and is paired with a practical hybrid estimation procedure that combines individual-level and joint panel estimation. We illustrate the estimation framework using a panel stochastic frontier model that treats the inefficiency term as a random effect, and show that it can be readily extended to a range of fixed effects specifications common in the literature. Simulation studies indicate strong finite-sample performance, and we further demonstrate the practicality of the approach in an empirical application to the cost efficiency of the U.S. commercial banking sector.