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Learning and filtering via simulation: smoothly jittered particle filters

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  • Neil Shephard
  • Thomas Flury

Abstract

A key ingredient of many particle filters is the use of the sampling importance resampling algorithm (SIR), which transforms a sample of weighted draws from a prior distribution into equally weighted draws from a posterior distribution.� We give a novel analysis of the SIR algorithm and analyse the jittered generalisation of SIR, showing that existing implementations of jittering lead to marked inferior behaviour over the base SIR algorithm.� We show how jittering can be designed to improve the performance of the SIR algorithm.� We illustrate its performance in practice in the context of three filtering problems.

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 469.

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Date of creation: 01 Dec 2009
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Handle: RePEc:oxf:wpaper:469

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Related research

Keywords: Importance sampling; Particle filter; Random numbers; Sampling importance resampling; State space models;

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  1. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  2. Eric Ghysels & Andrew Harvey & Éric Renault, 1995. "Stochastic Volatility," CIRANO Working Papers, CIRANO 95s-49, CIRANO.
  3. Shephard, Neil (ed.), 2005. "Stochastic Volatility: Selected Readings," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780199257201, October.
  4. Jones, M. C., 1990. "The performance of kernel density functions in kernel distribution function estimation," Statistics & Probability Letters, Elsevier, Elsevier, vol. 9(2), pages 129-132, February.
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Cited by:
  1. Andreasen, Martin & Meldrum, Andrew, 2013. "Likelihood inference in non-linear term structure models: the importance of the lower bound," Bank of England working papers, Bank of England 481, Bank of England.
  2. Duc Pham-Hi, 2014. "Shadow banking dynamics and learning behaviour," EcoMod2014 6920, EcoMod.
  3. Andras Fulop & Junye Li & Jun Yu, 2012. "Investigating Impacts of Self-Exciting Jumps in Returns and Volatility: A Bayesian Learning Approach," Global COE Hi-Stat Discussion Paper Series, Institute of Economic Research, Hitotsubashi University gd12-264, Institute of Economic Research, Hitotsubashi University.
  4. Andras Fulop & Junye Li & Jun Yu, 2012. "Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility," Working Papers, Singapore Management University, School of Economics 03-2012, Singapore Management University, School of Economics.

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