Sheng Dai, Timo Kuosmanen, Xun Zhou
arXiv 26 Jun 2025 · Statistics — Methodology
arXiv:2506.21110 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kuosmanen (2008) Representation theorem for convex nonparametric least squares self | 1.000 | 6 | 3 | 100% |
| 2 | Seijo and Sen (2011) Nonparametric least squares estimation of a multivariate convex regression function | 0.941 | 6 | 4 | 83% |
| 3 | Collard-Wexler and De Loecker (2016) Production function estimation with measurement error in inputs | 0.843 | 3 | 3 | 100% |
| 4 | Olley and Pakes (1996) The dynamics of productivity in the telecommunications equipment industry | 0.843 | 3 | 3 | 100% |
| 5 | Afriat (1967) The construction of utility functions from expenditure data | 0.811 | 4 | 2 | 100% |
| 6 | Afriat (1972) Efficiency estimation of production functions | 0.737 | 3 | 2 | 100% |
| 7 | Levinsohn and Petrin (2003) Estimating production functions using inputs to control for unobservables | 0.737 | 3 | 2 | 100% |
| 8 | Yatchew (2003) Semiparametric regression for the applied econometrician | 0.737 | 3 | 2 | 100% |
| 9 | Ackerberg, Caves and Frazer (2015) Identification properties of recent production function estimators | 0.644 | 2 | 2 | 100% |
| 10 | Chetverikov and Wilhelm (2017) Nonparametric instrumental variable estimation under monotonicity | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.