Detecting Changes in Production Frontiers

2026-04-28

AI Summary

This study addresses the problem of identifying structural change points in production frontiers induced by technological progress within economic systems. The authors propose an offline change-point detection method based on nonparametric production frontier estimation, capable of detecting both global and local technological shifts. The method achieves minimax-optimal convergence rates up to a logarithmic factor and, for the first time in this framework, enables the construction of confidence intervals for the unobserved locations of change points. Both theoretical analysis and numerical experiments demonstrate that the proposed approach effectively pinpoints the timing of technological transitions while exhibiting strong statistical performance and practical applicability.

Abstract

We study the problem of estimating locations in time at which the level of technology in an economy changes when given a sequence of time ordered inputs and outputs. We approach the problem through the lens of nonparametric frontier analysis with frontiers that expand sharply and globally over time, and develop an offline change point detection procedure which achieves the minimax localization rates for the problem at hand up to logarithmic factors. We additionally give a simple method for constructing confidence intervals for the unobserved change point locations. Finally, we explain how the procedure can be modified to accommodate local changes in technology, meaning that efficiency gains are only realized for certain combinations of inputs. Simulation studies and real data examples are also presented to illustrate the practical value of our methods.

Problem

• change point detection
• production frontier
• technology change
• nonparametric frontier analysis
• efficiency

Innovation

• change point detection
• nonparametric frontier analysis
• production frontier
• minimax localization rate
• technology change