A survey of sequential Monte Carlo methods for economics and finance

Drew Creal

No 18, Serie Research Memoranda from VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics

Abstract: This paper 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 paper 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.

Keywords: state space models; sequential Monte Carlo; particle filter; Markov chain Monte Carlo; Kalman filter (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Journal Article: A Survey of Sequential Monte Carlo Methods for Economics and Finance (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:vua:wpaper:2009-18

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