Quasi-Bayesian Inference for Production Frontiers

TL;DR

This paper introduces a quasi-Bayesian approach for inference on production frontiers, effectively combining extreme quantile estimates to produce reliable point estimates and confidence intervals, with proven asymptotic properties and demonstrated finite sample performance.

Contribution

It presents a novel quasi-Bayesian method that integrates multiple extreme quantile estimates for improved inference on production frontiers, with theoretical validation and empirical testing.

Findings

The method provides consistent point estimates of the production frontier.

Confidence intervals derived are asymptotically valid.

Finite sample simulations show good performance.

Abstract

We propose a quasi-Bayesian method to conduct inference for the production frontier. This approach combines multiple first-stage extreme quantile estimates by the quasi-Bayesian method to produce the point estimate and confidence interval for the production frontier. We show the asymptotic properties of the proposed estimator and the validity of the inference procedure. The finite sample performance of our method is illustrated through simulations and an empirical application.