Bayesian estimation of agent-based models via adaptive particle Markov chain Monte Carlo
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- Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017.
"Bayesian estimation of agent-based models,"
Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
- Jakob Grazzini & Matteo G. Richiardi & Mike Tsionas, 2015. "Bayesian Estimation of Agent-Based Models," LABORatorio R. Revelli Working Papers Series 145, LABORatorio R. Revelli, Centre for Employment Studies.
- Jakob Grazzini & Matteo Richiardi & Mike Tsionas, 2015. "Bayesian Estimation of Agent-Based Models," Economics Papers 2015-W12, Economics Group, Nuffield College, University of Oxford.
- 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.
- Zhang, Jingjing & Dennis, Todd E. & Landers, Todd J. & Bell, Elizabeth & Perry, George L.W., 2017. "Linking individual-based and statistical inferential models in movement ecology: A case study with black petrels (Procellaria parkinsoni)," Ecological Modelling, Elsevier, vol. 360(C), pages 425-436.
- 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.
- Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008.
"Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach,"
Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
- Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2005. "Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Economics Working Papers 2005-14, Christian-Albrechts-University of Kiel, Department of Economics.
- Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2006. "Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Economics Working Papers 2006-16, Christian-Albrechts-University of Kiel, Department of Economics.
- Edward P. Herbst & Frank Schorfheide, 2016. "Bayesian Estimation of DSGE Models," Economics Books, Princeton University Press, edition 1, number 10612.
- Nils Bertschinger & Iurii Mozzhorin & Sitabhra Sinha, 2018. "Reality-check for Econophysics: Likelihood-based fitting of physics-inspired market models to empirical data," Papers 1803.03861, arXiv.org.
- Franke, Reiner & Westerhoff, Frank, 2012.
"Structural stochastic volatility in asset pricing dynamics: Estimation and model contest,"
Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1193-1211.
- Franke, Reiner & Westerhoff, Frank, 2011. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," BERG Working Paper Series 78, Bamberg University, Bamberg Economic Research Group.
- 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.
- A. Doucet & M. K. Pitt & G. Deligiannidis & R. Kohn, 2015. "Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator," Biometrika, Biometrika Trust, vol. 102(2), pages 295-313.
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Keywords
Agents-based models; Makov chain Monte Carlo; particle filter;JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2020-03-02 (Econometrics)
- NEP-HME-2020-03-02 (Heterodox Microeconomics)
- NEP-ORE-2020-03-02 (Operations Research)
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