On‐line inference for hidden Markov models via particle filters
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DOI: 10.1111/1467-9868.00421
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Cited by:
- Eric Ruggieri, 2018. "A pruned recursive solution to the multiple change point problem," Computational Statistics, Springer, vol. 33(2), pages 1017-1045, June.
- Laurent-Emmanuel Calvet & Veronika Czellar, 2011.
"State-Observation Sampling and the Econometrics of Learning Models,"
Working Papers
hal-00625500, HAL.
- Calvet, Laurent-Emmanuel & Czellar , Veronika, 2011. "state-observation sampling and the econometrics of learning models," HEC Research Papers Series 947, HEC Paris.
- Laurent E. Calvet & Veronika Czellar, 2011. "State-Observation Sampling and the Econometrics of Learning Models," Papers 1105.4519, arXiv.org.
- 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.
- 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.
- Rimstad, Kjartan & Omre, Henning, 2013. "Approximate posterior distributions for convolutional two-level hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 187-200.
- António A. F. Santos, 2015. "On the Forecasting of Financial Volatility Using Ultra-High Frequency Data," GEMF Working Papers 2015-17, GEMF, Faculty of Economics, University of Coimbra.
- Rutger Jan Lange, 2020. "Bellman filtering for state-space models," Tinbergen Institute Discussion Papers 20-052/III, Tinbergen Institute, revised 19 May 2021.
- Celik, Nurcin & Son, Young-Jun, 2011. "State estimation of a shop floor using improved resampling rules for particle filtering," International Journal of Production Economics, Elsevier, vol. 134(1), pages 224-237, November.
- Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
- 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.
- Karppinen, Santeri & Rajala, Tuomas & Mäntyniemi, Samu & Kojola, Ilpo & Vihola, Matti, 2022. "Identifying territories using presence-only citizen science data: An application to the Finnish wolf population," Ecological Modelling, Elsevier, vol. 472(C).
- Zhao, Yunfei & Gao, Wei & Smidts, Carol, 2021. "Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
- Baranowski, Rafal & Chen, Yining & Fryzlewicz, Piotr, 2024. "Multiscale autoregression on adaptively detected timescales," LSE Research Online Documents on Economics 126054, London School of Economics and Political Science, LSE Library.
- Oliver Morell & Dennis Otto & Roland Fried, 2013. "On robust cross-validation for nonparametric smoothing," Computational Statistics, Springer, vol. 28(4), pages 1617-1637, August.
- Karamé, Frédéric, 2018.
"A new particle filtering approach to estimate stochastic volatility models with Markov-switching,"
Econometrics and Statistics, Elsevier, vol. 8(C), pages 204-230.
- Frédéric Karamé, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Post-Print hal-02296093, HAL.
- Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
- Panayotis Michaelides & Mike Tsionas & Panos Xidonas, 2020. "A Bayesian Signals Approach for the Detection of Crises," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 551-585, September.
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