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Particle filters for continuous likelihood evaluation and maximisation

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Cited by:

  1. Antonio A. F. Santos, 2021. "Bayesian Estimation for High-Frequency Volatility Models in a Time Deformed Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 455-479, February.
  2. Lux, Thomas, 2022. "Inference for Nonlinear State Space Models: A Comparison of Different Methods applied to Markov-Switching Multifractal Models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 69-95.
  3. Sophocles Mavroeidis, 2021. "Identification at the Zero Lower Bound," Econometrica, Econometric Society, vol. 89(6), pages 2855-2885, November.
  4. 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.
  5. Name 1 Dieter Wang Email 1 & Iman (I.P.P.) van Lelyveld & Julia (J.) Schaumburg, 2018. "Do information contagion and business model similarities explain bank credit risk commonalities?," Tinbergen Institute Discussion Papers 18-100/IV, Tinbergen Institute.
  6. Aleksejs Krecetovs & Pasquale Della Corte, 2016. "Macro uncertainty and currency premia," 2016 Meeting Papers 624, Society for Economic Dynamics.
  7. Bretó, Carles, 2014. "On idiosyncratic stochasticity of financial leverage effects," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 20-26.
  8. Liu Xiangdong & Li Xianglong & Zheng Shaozhi & Qian Hangyong, 2020. "PMCMC for Term Structure of Interest Rates under Markov Regime Switching and Jumps," Journal of Systems Science and Information, De Gruyter, vol. 8(2), pages 159-169, April.
  9. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
  10. 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.
  11. Johan Dahlin & Thomas B. Schon, 2015. "Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models," Papers 1511.01707, arXiv.org, revised Mar 2019.
  12. Creal, Drew D. & Wu, Jing Cynthia, 2015. "Estimation of affine term structure models with spanned or unspanned stochastic volatility," Journal of Econometrics, Elsevier, vol. 185(1), pages 60-81.
  13. Michael Pitt & Sheheryar Malik & Arnaud Doucet, 2014. "Simulated likelihood inference for stochastic volatility models using continuous particle filtering," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 527-552, June.
  14. Jin-Chuan Duan & Weiqi Zhang, 2014. "Forward-Looking Market Risk Premium," Management Science, INFORMS, vol. 60(2), pages 521-538, February.
  15. Lange, Rutger-Jan, 2024. "Bellman filtering and smoothing for state–space models," Journal of Econometrics, Elsevier, vol. 238(2).
  16. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
  17. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
  18. Lux, Thomas, 2018. "Estimation of agent-based models using sequential Monte Carlo methods," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 391-408.
  19. 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.
  20. Turnbull, Kathryn & Nemeth, Christopher & Nunes, Matthew & McCormick, Tyler, 2023. "Sequential estimation of temporally evolving latent space network models," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  21. Takefumi Yamazaki, 2018. "Financial friction sources in emerging economies: Structural estimation of sovereign default models," Discussion papers ron303, Policy Research Institute, Ministry of Finance Japan.
  22. Wu, Xinyu & Wang, Xiaona, 2020. "Forecasting volatility using realized stochastic volatility model with time-varying leverage effect," Finance Research Letters, Elsevier, vol. 34(C).
  23. Sergei Seleznev, 2016. "Solving DSGE models with stochastic trends," Bank of Russia Working Paper Series wps15, Bank of Russia.
  24. Lux, Thomas, 2017. "Estimation of agent-based models using sequential Monte Carlo methods," Economics Working Papers 2017-07, Christian-Albrechts-University of Kiel, Department of Economics.
  25. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
  26. Januj Amar Juneja, 2022. "A Computational Analysis of the Tradeoff in the Estimation of Different State Space Specifications of Continuous Time Affine Term Structure Models," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 173-220, June.
  27. Wee, Damien C.H. & Chen, Feng & Dunsmuir, William T.M., 2022. "Likelihood inference for Markov switching GARCH(1,1) models using sequential Monte Carlo," Econometrics and Statistics, Elsevier, vol. 21(C), pages 50-68.
  28. Kleppe, Tore Selland & Oglend, Atle, 2017. "Estimating the competitive storage model: A simulated likelihood approach," Econometrics and Statistics, Elsevier, vol. 4(C), pages 39-56.
  29. Lux, Thomas, 2018. "Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models," Economics Working Papers 2018-07, Christian-Albrechts-University of Kiel, Department of Economics.
  30. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.
  31. Name 1 Dieter Wang Email 1 & Iman (I.P.P.) van Lelyveld & Julia (J.) Schaumburg, 2018. "Do information contagion and business model similarities explain bank credit risk commonalities?," Tinbergen Institute Discussion Papers 18-100/IV, Tinbergen Institute.
  32. Jonathan R. Stroud & Michael S. Johannes, 2014. "Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1368-1384, December.
  33. Pitt, Michael K. & Silva, Ralph dos Santos & Giordani, Paolo & Kohn, Robert, 2012. "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter," Journal of Econometrics, Elsevier, vol. 171(2), pages 134-151.
  34. Fulop, Andras & Li, Junye, 2019. "Bayesian estimation of dynamic asset pricing models with informative observations," Journal of Econometrics, Elsevier, vol. 209(1), pages 114-138.
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