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A survey of sequential Monte Carlo methods for economics and finance

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  • 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.

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Bibliographic Info

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

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Date of creation: 2009
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Handle: RePEc:dgr:vuarem:2009-18

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Web page: http://www.feweb.vu.nl

Related research

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

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References

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  1. Charles S. Bos & Neil Shephard, 2004. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Economics Papers 2004-W02, Economics Group, Nuffield College, University of Oxford.
  2. 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.
  3. Cappé, Olivier & Guillin, Arnaud & Marin, Jean-Michel & Robert, Christian P., 2004. "Population Monte Carlo," Economics Papers from University Paris Dauphine 123456789/6072, Paris Dauphine University.
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  5. 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.
  6. Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series 2008fe32, Oxford Financial Research Centre.
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  28. 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.
  29. Nicolas Chopin, 2002. "Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference," Working Papers 2002-44, Centre de Recherche en Economie et Statistique.
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Citations

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Cited by:
  1. Luati, Alessandra & Proietti, Tommaso, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Working Papers 02 BAWP, University of Sydney Business School, Discipline of Business Analytics.
  2. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "Extracting a Robust U.S. Business Cycle Using a Time-Varying Multivariate Model-Based Bandpass Filter," Working Papers UWEC-2008-15-FC, University of Washington, Department of Economics.
  3. Arnaud Doucet & Neil Shephard, 2012. "Robust inference on parameters via particle filters and sandwich covariance matrices," Economics Papers 2012-W05, Economics Group, Nuffield College, University of Oxford.
  4. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, vol. 177(2), pages 213-232.
  5. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, School of Economics and Management, University of Aarhus.
  6. Rhys Bidder & Matthew E. Smith, 2013. "Doubts and variability: a robust perspective on exotic consumption series," Working Paper Series 2013-28, Federal Reserve Bank of San Francisco.
  7. James M. Nason & Gregor W. Smith, 2014. "Measuring the Slowly Evolving Trend in US Inflation with Professional Forecasts," CAMA Working Papers 2014-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  8. Christopher Gust & David Lopez-Salido & Matthew E. Smith, 2012. "The empirical implications of the interest-rate lower bound," Finance and Economics Discussion Series 2012-83, Board of Governors of the Federal Reserve System (U.S.).
  9. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
  10. Neil Shephard, 2013. "Martingale unobserved component models," Economics Series Working Papers 644, University of Oxford, Department of Economics.
  11. James M. Nason & Gregor W. Smith, 2013. "Reverse Kalman filtering U.S. inflation with sticky professional forecasts," Working Papers 13-34, Federal Reserve Bank of Philadelphia.
  12. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo Matlab Toolbox," Working Papers 2013:08, Department of Economics, University of Venice "Ca' Foscari".
  13. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
  14. Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.

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