Orthogonality conditions for convex regression
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.
Cite
@article{arxiv.2506.21110,
title = {Orthogonality conditions for convex regression},
author = {Sheng Dai and Timo Kuosmanen and Xun Zhou},
journal= {arXiv preprint arXiv:2506.21110},
year = {2025}
}