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Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models

Author

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  • Domenico Delli Gatti
  • Jakob Grazzini

Abstract

We propose two novel methods to “bring ABMs to the data”. First, we put forward a new Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time series using data obtained from the simulation of an ABM. We apply our methodological contributions to a medium-scale macro agent-based model. We show that the estimated model is capable of reproducing features of observed data and of forecasting one-period ahead output-gap and investment with a remarkable degree of accuracy.

Suggested Citation

  • Domenico Delli Gatti & Jakob Grazzini, 2019. "Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models," CESifo Working Paper Series 7894, CESifo Group Munich.
    • Handle: RePEc:ces:ceswps:_7894
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    File URL: https://www.cesifo-group.de/DocDL/cesifo1_wp7894_0.pdf
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    References listed on IDEAS

    as
    1. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01499344, HAL.
    2. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    3. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
    4. repec:eee:jeborg:v:157:y:2019:i:c:p:101-116 is not listed on IDEAS
    5. Dosi, Giovanni & Fagiolo, Giorgio & Napoletano, Mauro & Roventini, Andrea, 2013. "Income distribution, credit and fiscal policies in an agent-based Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1598-1625.
    6. 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.
    7. Isabelle Salle & Murat Yıldızoğlu, 2014. "Efficient Sampling and Meta-Modeling for Computational Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 507-536, December.
    8. repec:eee:dyncon:v:85:y:2017:i:c:p:21-45 is not listed on IDEAS
    9. Guerini, Mattia & Napoletano, Mauro & Roventini, Andrea, 2018. "No man is an Island: The impact of heterogeneity and local interactions on macroeconomic dynamics," Economic Modelling, Elsevier, vol. 68(C), pages 82-95.
    10. repec:eee:dyncon:v:91:y:2018:i:c:p:391-408 is not listed on IDEAS
    11. Donovan Platt, 2019. "A Comparison of Economic Agent-Based Model Calibration Methods," Papers 1902.05938, arXiv.org.
    12. Assenza, Tiziana & Delli Gatti, Domenico & Grazzini, Jakob, 2015. "Emergent dynamics of a macroeconomic agent based model with capital and credit," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 5-28.
    13. repec:oup:indcch:v:27:y:2018:i:6:p:1069-1090. is not listed on IDEAS
    14. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    15. Gilli, M. & Winker, P., 2003. "A global optimization heuristic for estimating agent based models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 299-312, March.
    16. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    17. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
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    More about this item

    Keywords

    agent-based models; estimation; forecasting;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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