This paper introduces Particle Swarm Optimization [PSO] to econometrics with focus on projection-based test inversion. Econometricians have developed such methods to enable a robust analysis of imperfectly identified models. Despite important theoretical breakthroughs, computational and numerical tool kits have not followed suit. This paper compares stochastic solvers including PSO on speed and accuracy for the problem. Empirically, the paper analyzes a three-equation New Keynesian model for the U.S.. Results are confirmed via a synthetic sample with relevant and weak instruments. In contrast to PSO, we find that popular solvers may converge to local optima suggesting misleading decisions on the nature of the New Keynesian Phillips Curve, determinacy of monetary policies, and the persistence of the Taylor rule. Results confirm that far more attention needs to be paid to numerical precision as test inversion duly gains popularity in applied econometrics.

本文介绍了粒子群算法[ PSO]到计量经济学，重点是基于投影的测试反演。计量经济学家已经开发出了这样的方法，可以对不完美识别的模型进行可靠的分析。尽管在理论上取得了重大突破，但计算和数值工具包并没有紧随其后。本文比较了包括PSO在内的随机求解器在速度和准确性上的问题。根据经验，本文分析了美国的三方程式新凯恩斯模型。通过相关和弱仪器的合成样品确认结果。与PSO相反，我们发现流行的求解器可能会收敛于局部最优，这暗示着对新凯恩斯式菲利普斯曲线的性质，货币政策的确定性和泰勒规则的持久性的误导性决定。