A Survey of Sequential Monte Carlo Methods for Economics and Finance
为经济学家介绍序贯蒙特卡洛方法（粒子滤波），解释其基本原理、文献引用和理论依据，适用于动态随机一般均衡模型和期权定价等应用。
This article serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation-based algorithms used to compute the high-dimensional and/or complex integrals that arise regularly in applied work. These methods are becoming increasingly popular in economics and finance; from dynamic stochastic general equilibrium models in macro-economics to option pricing. The objective of this article is to explain the basics of the methodology, provide references to the literature, and cover some of the theoretical results that justify the methods in practice.