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.

翻译：结构性估算的一个实际挑战是,要求准确尽量减少一个往往非光滑、非节流或两者兼而有之的抽样客观功能。本文件提出一个简单的算法,旨在找到准确的解决方案,而不进行详尽的搜索。它以网格搜索步骤补充新的高斯-纽顿算法的每一次迭代。一个有限的抽样分析,仅使用计量经济学假设,同时得出其优化和统计特性。经过有限的迭代,算法自动从全球向快速本地趋同过渡,产生准确的估计数,概率很高。模拟实例和实证应用说明了结果。