Dynamic Perturbation
提出一种名为动态扰动的新算法，通过沿均衡路径计算政策函数的一阶泰勒展开，以更高精度求解大规模宏观经济模型，尤其适用于强非线性和偶尔约束（如零下限）的模型。
Abstract We present a novel algorithm called Dynamic Perturbation for solving large-scale macroeconomic models. Our approach involves computing first-order Taylor expansions of the policy functions along the entire equilibrium path. This method applies to a wide range of models and offers significantly higher accuracy than traditional perturbation approaches. Remarkably, even when utilising first-order approximations, our method can effectively handle models with strong nonlinearities and occasionally binding constraints, such as the zero lower bound.