Learning and filtering via simulation: smoothly jittered particle filters
AbstractA 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 InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 469.
Date of creation: 01 Dec 2009
Date of revision:
Importance sampling; Particle filter; Random numbers; Sampling importance resampling; State space models;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-05 (All new papers)
- NEP-CMP-2010-02-05 (Computational Economics)
- NEP-ECM-2010-02-05 (Econometrics)
- NEP-ETS-2010-02-05 (Econometric Time Series)
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