A SURVEY OF SEQUENTIAL MONTE CARLO METHODS FOR ECONOMICS AND FINANCE

被引：139
作者：
Creal, Drew [1 ]
机构：
[1] Univ Chicago, Booth Sch Business, Chicago, IL 60637 USA
来源：
ECONOMETRIC REVIEWS | 2012年 / 31卷 / 03期
关键词：
Kalman filter; Markov chain Monte Carlo; Particle filter; Sequential Monte Carlo; State space models; CENTRAL-LIMIT-THEOREM; HIDDEN MARKOV-MODELS; STATE-SPACE MODELS; TIME-SERIES; STOCHASTIC VOLATILITY; PARTICLE FILTERS; PROBABILISTIC FUNCTIONS; PARAMETER-ESTIMATION; SIMULATION SMOOTHER; BAYESIAN-INFERENCE;
D O I：
10.1080/07474938.2011.607333
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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.
引用
收藏
页码：245 / 296
页数：52
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