# Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models

arXiv: https://arxiv.org/abs/2301.07196 · HTML: https://mlsanity.com/paper/2301.07196/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Study](https://mlsanity.com/paper/2505.11752/) (2025)

## Abstract (arXiv)

A practical challenge for structural estimation is the requirement to accurately minimize a sample objective function which is often non-smooth, non-convex, or both. This paper proposes a simple algorithm designed to find accurate solutions without performing an exhaustive search. It augments each iteration from a new Gauss-Newton algorithm with a grid search step. A finite sample analysis derives its optimization and statistical properties simultaneously using only econometric assumptions. After a finite number of iterations, the algorithm automatically transitions from global to fast local convergence, producing accurate estimates with high probability. Simulated examples and an empirical application illustrate the results.