Orthogonality conditions for convex regression

TL;DR

This paper establishes orthogonality conditions for convex regression models, including additive and multiplicative forms, and proposes a hybrid instrumental variable approach to address endogeneity, validated through simulations and real data.

Contribution

It introduces the first sample orthogonality conditions for convex regression and develops a hybrid IV control function method for endogeneity correction.

Findings

Orthogonality conditions derived for convex regression models.

Hybrid IV approach effectively mitigates endogeneity bias.

Empirical application demonstrates practical utility.

Abstract

Econometric identification generally relies on orthogonality conditions, which usually state that the random error term is uncorrelated with the explanatory variables. In convex regression, the orthogonality conditions for identification are unknown. Applying Lagrangian duality theory, we establish the sample orthogonality conditions for convex regression, including additive and multiplicative formulations of the regression model, with and without monotonicity and homogeneity constraints. We then propose a hybrid instrumental variable control function approach to mitigate the impact of potential endogeneity in convex regression. The superiority of the proposed approach is shown in a Monte Carlo study and examined in an empirical application to Chilean manufacturing data.