Piece-wise linear isotonic regression

TL;DR

This paper introduces a piece-wise linear smoothing method for isotonic regression that enhances the interpretability of marginal properties like elasticities, especially in non-convex scenarios.

Contribution

It proposes a novel bilevel optimization framework to recover meaningful marginal estimates from isotonic regression, extending its applicability to non-convex functions.

Findings

Significantly improves estimation accuracy in convex and non-convex settings.

Effective for univariate and multivariate data.

Demonstrated practical utility in analyzing Finnish municipalities.

Abstract

Isotonic regression provides a flexible, tuning-free approach to estimating monotonic functions without imposing global curvature constraints, yet the estimated regression function is inherently a step function. This paper addresses a key limitation of such estimators: their inability to provide meaningful marginal properties, such as shadow prices or elasticities. We propose a novel piece-wise linear smoothing framework that recovers meaningful marginal estimates even in non-convex settings. Building on the concept of conditional convexity originally developed in deterministic frontier analysis, we formulate the smoothing process as a bilevel optimization problem that fits a continuous, monotonic, piece-wise linear function to the initial isotonic regression predictions. Monte Carlo simulations demonstrate that the proposed approach can significantly improve estimation accuracy in both convex and non-convex settings for univariate and multivariate data. We apply this approach to analyze agglomeration economies in Finnish municipalities, illustrating its practical value.