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Practical filtering with sequential parameter learning

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  • Nicholas G. Polson
  • Jonathan R. Stroud
  • Peter Müller
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    Abstract

    The paper develops a simulation-based approach to sequential parameter learning and filtering in general state space models. Our approach is based on approximating the target posterior by a mixture of fixed lag smoothing distributions. Parameter inference exploits a sufficient statistic structure and the methodology can be easily implemented by modifying state space smoothing algorithms. We avoid reweighting particles and hence sample degeneracy problems that plague particle filters that use sequential importance sampling. The method is illustrated by using two examples: a benchmark auto-regressive model with observation error and a high dimensional dynamic spatiotemporal model. We show that the method provides accurate inference in the presence of outliers, model misspecification and high dimensionality. Copyright (c) 2008 The Authors.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Volume (Year): 70 (2008)
    Issue (Month): 2 ()
    Pages: 413-428

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    Handle: RePEc:bla:jorssb:v:70:y:2008:i:2:p:413-428

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    Cited by:
    1. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. Laurent E. Calvet & Veronika Czellar, 2011. "State-Observation Sampling and the Econometrics of Learning Models," Papers 1105.4519, arXiv.org.
    3. Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers 09-31, Bank of Canada.
    4. Ren-Her Wang & John Aston & Cheng-Der Fuh, 2010. "The Role of Additional Information in Option Pricing: Estimation Issues for the State Space Model," Computational Economics, Society for Computational Economics, vol. 36(4), pages 283-307, December.

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