pyStoNED: A Python Package for Convex Regression and Frontier Estimation

TL;DR

pyStoNED is a Python package that facilitates shape-constrained nonparametric regression, enabling efficient estimation of production frontiers and related functions, thus bridging the gap between advanced econometric techniques and empirical practice.

Contribution

It introduces a comprehensive, open-source Python tool for multivariate convex regression and related methods, enhancing accessibility and computational efficiency in frontier analysis.

Findings

Provides a user-friendly implementation of convex regression techniques.

Demonstrates application in estimating frontier cost and production functions.

Addresses computational challenges in shape-constrained nonparametric regression.

Abstract

Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning and related fields. In the field of productivity and efficiency analysis, recent developments in the multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression have bridged the long-standing gap between the conventional deterministic-nonparametric and stochastic-parametric methods. Unfortunately, the heavy computational burden and the lack of powerful, reliable, and fully open access computational package has slowed down the diffusion of these advanced estimation techniques to the empirical practice. The purpose of the Python package pyStoNED is to address this challenge by providing a freely available and user-friendly tool for the multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. This paper presents a tutorial of the pyStoNED package and illustrates its application, focusing on the estimation of frontier cost and production functions.