Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models
Author
Abstract
Suggested Citation
DOI: 10.1007/s00180-017-0770-y
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Herbst, Edward & Schorfheide, Frank, 2019.
"Tempered particle filtering,"
Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
- Edward Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," PIER Working Paper Archive 16-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Oct 2016.
- Edward P. Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," Finance and Economics Discussion Series 2016-072, Board of Governors of the Federal Reserve System (U.S.).
- Edward Herbst & Frank Schorfheide, 2017. "Tempered Particle Filtering," NBER Working Papers 23448, National Bureau of Economic Research, Inc.
- 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.
- 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.
- repec:dau:papers:123456789/5724 is not listed on IDEAS
- 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.
- 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.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- 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).
- 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.- Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
- 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.
- 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.
- Herbst, Edward & Schorfheide, Frank, 2019.
"Tempered particle filtering,"
Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
- Edward P. Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," Finance and Economics Discussion Series 2016-072, Board of Governors of the Federal Reserve System (U.S.).
- Edward Herbst & Frank Schorfheide, 2017. "Tempered Particle Filtering," NBER Working Papers 23448, National Bureau of Economic Research, Inc.
- Edward Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," PIER Working Paper Archive 16-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Oct 2016.
- Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.
- Wolf, Elias, 2022. "Estimating growth at risk with skewed stochastic volatility models," Discussion Papers 2022/2, Free University Berlin, School of Business & Economics.
- 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.
- Pablo A. Cuba-Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood Evaluation of Models with Occasionally Binding Constraints," Finance and Economics Discussion Series 2019-028, Board of Governors of the Federal Reserve System (U.S.).
- repec:dau:papers:123456789/11429 is not listed on IDEAS
- Sergei Seleznev, 2016. "Solving DSGE models with stochastic trends," Bank of Russia Working Paper Series wps15, Bank of Russia.
- 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.
- Ioannis Bournakis & Mike Tsionas, 2024.
"A Non‐parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax,"
Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 641-671, June.
- 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.
- 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.
- S. Boragan Aruoba & Pablo A. Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2020. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Working Papers 20-13, Federal Reserve Bank of Philadelphia.
- Schorfheide, Frank & Aruoba, Boragan & Cuba-Borda, Pablo & Hilga-Flores, Kenji & Villalvazo, Sergio, 2020. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," CEPR Discussion Papers 15388, C.E.P.R. Discussion Papers.
- S. Boragan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2020. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," PIER Working Paper Archive 20-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- S. Boragan Aruoba & Pablo A. Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2020. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," International Finance Discussion Papers 1272, Board of Governors of the Federal Reserve System (U.S.).
- S. Borağan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2020. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," NBER Working Papers 27991, National Bureau of Economic Research, Inc.
- Arellano, Manuel & Blundell, Richard & Bonhomme, Stéphane & Light, Jack, 2024.
"Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence,"
Journal of Econometrics, Elsevier, vol. 240(2).
- Manuel Arellano & Richard Blundell & Stéphane Bonhomme & Jack Light, 2023. "Heterogeneity of Consumption Responses to Income Shocks in the Presence of Nonlinear Persistence," Working Papers wp2023_2301, CEMFI.
- Manuel Arellano & Richard Blundell & Stéphane Bonhomme & Jack Light, 2024. "Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence," Post-Print hal-04536563, HAL.
- Manuel Arellano & Richard Blundell & Stéphane Bonhomme & Jack Light, 2023. "Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence," CeMMAP working papers 07/23, Institute for Fiscal Studies.
- Arellano, Manuel & Blundell, Richard & Bonhomme, Stéphane & Light, Jack, 2023. "Heterogeneity of Consumption Responses to Income Shocks in the Presence of Nonlinear Persistence," TSE Working Papers 23-1435, Toulouse School of Economics (TSE).
- 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.
- 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.
- Joshua Chan & Arnaud Doucet & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 18-12, National Graduate Institute for Policy Studies.
- Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate stochastic volatility with co-heteroscedasticity," CAMA Working Papers 2018-52, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- CHAN Joshua & DOUCET Arnaud & Roberto Leon-Gonzalez & STRACHAN Rodney W., 2020. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 20-09, National Graduate Institute for Policy Studies.
- 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.
- Arnaud Dufays, 2016.
"Evolutionary Sequential Monte Carlo Samplers for Change-Point Models,"
Econometrics, MDPI, vol. 4(1), pages 1-33, March.
- Arnaud Dufays, 2015. "Evolutionary Sequential Monte Carlo Samplers for Change-point Models," Cahiers de recherche 1518, CIRPEE.
- Arnaud Dufays, 2015. "Evolutionary Sequential Monte Carlo Samplers for Change-point Models," Cahiers de recherche 1508, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
- Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
- 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.
- Kouritzin, Michael A., 2017. "Residual and stratified branching particle filters," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 145-165.
- 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.
More about this item
Keywords
Hidden Markov model; Maximum likelihood; Particle filter; SAEM; Sequential Monte Carlo; Stochastic differential equation;All these keywords.
Statistics
Access and download statisticsCorrections
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.