Abstract
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.
Original language | English (US) |
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Pages (from-to) | 245-296 |
Number of pages | 52 |
Journal | Econometric Reviews |
Volume | 31 |
Issue number | 3 |
DOIs | |
State | Published - May 2012 |
Externally published | Yes |
Keywords
- Kalman filter
- Markov chain Monte Carlo
- Particle filter
- Sequential Monte Carlo
- State space models
ASJC Scopus subject areas
- Economics and Econometrics