This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

A survey of sequential Monte Carlo methods for economics and finance

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Creal, D. (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)
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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: ftp://zappa.ubvu.vu.nl/20090018.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics in its series Serie Research Memoranda with number 0018.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 2009
Date of revision:
Handle: RePEc:dgr:vuarem:2009-18

Contact details of provider:
Web page: http://www.feweb.vu.nl

For technical questions regarding this item, or to correct its listing, contact: (R. Dam).

Related research
Keywords: state space models; sequential Monte Carlo; particle filter; Markov chain Monte Carlo; Kalman filter;

Other versions of this item:

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Nicolas Chopin, 2007. "Inference and model choice for sequentially ordered hidden Markov models," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 269-284. [Downloadable!] (restricted)
  2. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November. [Downloadable!] (restricted)
  3. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Oxford University Press for Biometrika Trust, vol. 89(3), pages 539-552, August.
  4. Jasra, Ajay & Doucet, Arnaud & Stephens, David A. & Holmes, Christopher C., 2008. "Interacting sequential Monte Carlo samplers for trans-dimensional simulation," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1765-1791, January. [Downloadable!] (restricted)
  5. Rong Chen & Jun S. Liu, 2000. "Mixture Kalman filters," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 493-508. [Downloadable!] (restricted)
  6. Arnaud Doucet & Vladislav Tadić, 2003. "Parameter estimation in general state-space models using particle methods," Annals of the Institute of Statistical Mathematics, Springer, vol. 55(2), pages 409-422, June. [Downloadable!] (restricted)
  7. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    Other versions:
  8. Yuguo Chen & Junyi Xie & Jun S. Liu, 2005. "Stopping-time resampling for sequential Monte Carlo methods," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 199-217. [Downloadable!] (restricted)
  9. Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," Economics Series Working Papers 413, University of Oxford, Department of Economics. [Downloadable!]
    Other versions:
  10. Liang F., 2002. "Dynamically Weighted Importance Sampling in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 807-821, September. [Downloadable!] (restricted)
  11. Godsill, Simon J. & Doucet, Arnaud & West, Mike, 2004. "Monte Carlo Smoothing for Nonlinear Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 156-168, January. [Downloadable!] (restricted)
  12. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March. [Downloadable!] (restricted)
  13. Pitt, Michael K, 2002. "Smooth Particle Filters for Likelihood Evaluation and Maximisation," The Warwick Economics Research Paper Series (TWERPS) 651, University of Warwick, Department of Economics. [Downloadable!]
  14. Christophe Andrieu & Arnaud Doucet, 2002. "Particle filtering for partially observed Gaussian state space models," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 827-836. [Downloadable!] (restricted)
  15. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Blackwell Publishing, vol. 74(4), pages 1059-1087, October. [Downloadable!] (restricted)
    Other versions:
  16. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June. [Downloadable!] (restricted)
  17. Nicholas G. Polson & Jonathan R. Stroud & Peter Müller, 2008. "Practical filtering with sequential parameter learning," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 413-428. [Downloadable!] (restricted)
  18. Paul Fearnhead & Peter Clifford, 2003. "On-line inference for hidden Markov models via particle filters," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 887-899. [Downloadable!] (restricted)
  19. Juan F. Rubio-Ramirez & Jesus Fernández-Villaverde, 2005. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 891-910. [Downloadable!]
    Other versions:
  20. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Blackwell Publishing, vol. 65(3), pages 361-93, July. [Downloadable!] (restricted)
    Other versions:
  21. Celeux, Gilles & Marin, Jean-Michel & Robert, Christian P., 2006. "Iterated importance sampling in missing data problems," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3386-3404, August. [Downloadable!] (restricted)
  22. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December. [Downloadable!] (restricted)
  23. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Oxford University Press for Biometrika Trust, vol. 89(3), pages 603-616, August.
  24. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor and Francis Journals, vol. 26(2-4), pages 113-172. [Downloadable!] (restricted)
    Other versions:
Full references

Statistics
Access and download statistics

Did you know? IDEAS was launched in September 1997.

This page was last updated on 2009-12-17.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.