IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v33y2018i1d10.1007_s00180-017-0770-y.html
   My bibliography  Save this article

Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models

Author

Listed:
  • Umberto Picchini

    (Lund University)

  • Adeline Samson

    (Universite Grenoble Alpes, LJK
    CNRS, LJK)

Abstract

We study the class of state-space models and perform maximum likelihood estimation for the model parameters. We consider a stochastic approximation expectation–maximization (SAEM) algorithm to maximize the likelihood function with the novelty of using approximate Bayesian computation (ABC) within SAEM. The task is to provide each iteration of SAEM with a filtered state of the system, and this is achieved using an ABC sampler for the hidden state, based on sequential Monte Carlo methodology. It is shown that the resulting SAEM-ABC algorithm can be calibrated to return accurate inference, and in some situations it can outperform a version of SAEM incorporating the bootstrap filter. Two simulation studies are presented, first a nonlinear Gaussian state-space model then a state-space model having dynamics expressed by a stochastic differential equation. Comparisons with iterated filtering for maximum likelihood inference, and Gibbs sampling and particle marginal methods for Bayesian inference are presented.

Suggested Citation

  • Umberto Picchini & Adeline Samson, 2018. "Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models," Computational Statistics, Springer, vol. 33(1), pages 179-212, March.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0770-y
    DOI: 10.1007/s00180-017-0770-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-017-0770-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-017-0770-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    2. Quentin J M Huys & Liam Paninski, 2009. "Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-16, May.
    3. repec:dau:papers:123456789/5724 is not listed on IDEAS
    4. Gael M. Martin & Brendan P.M. McCabe & David T. Frazier & Worapree Maneesoonthorn & Christian P. Robert, 2016. "Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 09/16, Monash University, Department of Econometrics and Business Statistics.
    5. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    6. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Salima El Kolei & Fabien Navarro, 2022. "Contrast estimation for noisy observations of diffusion processes via closed-form density expansions," Statistical Inference for Stochastic Processes, Springer, vol. 25(2), pages 303-336, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    2. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    3. Wolf, Elias, 2023. "Estimating Growth at Risk with Skewed Stochastic Volatility Models," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277696, Verein für Socialpolitik / German Economic Association.
    4. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    5. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.
    6. Wolf, Elias, 2022. "Estimating growth at risk with skewed stochastic volatility models," Discussion Papers 2022/2, Free University Berlin, School of Business & Economics.
    7. Pablo Cuba‐Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood evaluation of models with occasionally binding constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1073-1085, November.
    8. repec:dau:papers:123456789/11429 is not listed on IDEAS
    9. Sergei Seleznev, 2016. "Solving DSGE models with stochastic trends," Bank of Russia Working Paper Series wps15, Bank of Russia.
    10. Sanha Noh, 2020. "Posterior Inference on Parameters in a Nonlinear DSGE Model via Gaussian-Based Filters," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 795-841, December.
    11. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    12. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    13. Diana Giurghita & Dirk Husmeier, 2018. "Statistical modelling of cell movement," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 265-280, August.
    14. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," Working Paper series 18-38, Rimini Centre for Economic Analysis.
    15. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    16. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    17. Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
    18. Andrew Hoegh & Frank T. Manen & Mark Haroldson, 2021. "Agent-Based Models for Collective Animal Movement: Proximity-Induced State Switching," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 560-579, December.
    19. Kouritzin, Michael A., 2017. "Residual and stratified branching particle filters," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 145-165.
    20. Lux, Thomas, 2020. "Bayesian estimation of agent-based models via adaptive particle Markov chain Monte Carlo," Economics Working Papers 2020-01, Christian-Albrechts-University of Kiel, Department of Economics.
    21. Zhang, Yixiao & Yu, Cindy L. & Li, Haitao, 2022. "Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach," Econometrics and Statistics, Elsevier, vol. 24(C), pages 75-93.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0770-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.