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Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference

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  • Nicolas Chopin

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  • Nicolas Chopin, 2002. "Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference," Working Papers 2002-44, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2002-44
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    References listed on IDEAS

    as
    1. 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.
    2. Nicolas Chopin, 2001. "Sequential Inference and State Number Determination for Discrete State-Space Models through Particle Filtering," Working Papers 2001-34, Center for Research in Economics and Statistics.
    3. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    4. 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, October.
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    Cited by:

    1. Jasra, Ajay & Doucet, Arnaud, 2008. "Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3062-3069, December.
    2. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    3. Gareth W. Peters & Efstathios Panayi & Francois Septier, 2015. "SMC-ABC methods for the estimation of stochastic simulation models of the limit order book," Papers 1504.05806, arXiv.org.
    4. Calvet, Laurent-Emmanuel & Czellar , Veronika, 2011. "state-observation sampling and the econometrics of learning models," HEC Research Papers Series 947, HEC Paris.
    5. Saikat Saha, 2015. "Noise Robust Online Inference for Linear Dynamic Systems," Papers 1504.05723, arXiv.org.
    6. Johansen, Adam M. & Doucet, Arnaud, 2008. "A note on auxiliary particle filters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1498-1504, September.
    7. Jie Xiong & Yong Zeng, 2011. "A branching particle approximation to a filtering micromovement model of asset price," Statistical Inference for Stochastic Processes, Springer, vol. 14(2), pages 111-140, May.
    8. John Geweke, 2016. "Sequentially Adaptive Bayesian Learning for a Nonlinear Model of the Secular and Cyclical Behavior of US Real GDP," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    9. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    10. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
    11. Qian, Hang, 2015. "Inequality Constrained State Space Models," MPRA Paper 66447, University Library of Munich, Germany.
    12. Markku Lanne & Jani Luoto, 2015. "Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints," CREATES Research Papers 2015-37, Department of Economics and Business Economics, Aarhus University.
    13. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
    14. Garland Durham & John Geweke, 2013. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Working Paper Series 9, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    15. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.
    16. Daniel F. Waggoner & Hongwei Wu & Tao Zha, 2014. "The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models," FRB Atlanta Working Paper 2014-21, Federal Reserve Bank of Atlanta.

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