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

Author

Listed:
  • 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.
    • Handle: RePEc:ces:ceswps:_7894
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    References listed on IDEAS

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    21. Donovan Platt, 2019. "A Comparison of Economic Agent-Based Model Calibration Methods," Papers 1902.05938, arXiv.org.
    22. 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.
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    24. T Assenza & P Colzani & D Delli Gatti & J Grazzini, 2018. "Does fiscal policy matter? Tax, transfer, and spend in a macro ABM with capital and credit," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(6), pages 1069-1090.
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    Citations

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    Cited by:

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    2. Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized neural networks for agent-based model forecasting," Papers 2308.05753, arXiv.org.
    3. Filippo Gusella, 2022. "Detecting And Measuring Financial Cycles In Heterogeneous Agents Models: An Empirical Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 25(02n03), pages 1-22, March.
    4. Filippo Gusella & Giorgio Ricchiuti, 2021. "State Space Model to Detect Cycles in Heterogeneous Agents Models," Working Papers - Economics wp2021_10.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    5. Zhang, Jinyu & Zhang, Qiaosen & Li, Yong & Wang, Qianchao, 2023. "Sequential Bayesian inference for agent-based models with application to the Chinese business cycle," Economic Modelling, Elsevier, vol. 126(C).
    6. Ciola, Emanuele & Gaffeo, Edoardo & Gallegati, Mauro, 2022. "Search for profits and business fluctuations: How does banks’ behaviour explain cycles?," Journal of Economic Dynamics and Control, Elsevier, vol. 135(C).
    7. Vandin, Andrea & Giachini, Daniele & Lamperti, Francesco & Chiaromonte, Francesca, 2022. "Automated and distributed statistical analysis of economic agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    8. Sylvain Barde, 2022. "Bayesian Estimation of Large-Scale Simulation Models with Gaussian Process Regression Surrogates," Studies in Economics 2203, School of Economics, University of Kent.
    9. Elizabeth Jane Casabianca & Alessia Lo Turco & Daniela Maggioni, 2021. "Migration And The Structure Of Manufacturing Production. A View From Italian Provinces," Working Papers 448, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    10. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    11. Corrado Monti & Marco Pangallo & Gianmarco De Francisci Morales & Francesco Bonchi, 2022. "On learning agent-based models from data," Papers 2205.05052, arXiv.org, revised Nov 2022.
    12. Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2021. "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," Papers 2102.05405, arXiv.org, revised Nov 2023.
    13. Severin Reissl, 2022. "Fiscal multipliers, expectations and learning in a macroeconomic agent‐based model," Economic Inquiry, Western Economic Association International, vol. 60(4), pages 1704-1729, October.
    14. Donovan Platt, 2022. "Bayesian Estimation of Economic Simulation Models Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 599-650, February.
    15. Domenico Delli Gatti & Severin Reissl, 2020. "ABC: An Agent Based Exploration of the Macroeconomic Effects of Covid-19," CESifo Working Paper Series 8763, CESifo.
    16. Mario Martinoli & Alessio Moneta & Gianluca Pallante, 2022. "Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search," LEM Papers Series 2022/33, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    17. Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021. "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers 450, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    18. Aldo Glielmo & Marco Favorito & Debmallya Chanda & Domenico Delli Gatti, 2023. "Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs," Papers 2302.11835, arXiv.org, revised Dec 2023.
    19. Filippo Gusella & Giorgio Ricchiuti, 2022. "A State-Space Approach for Time-Series Prediction of an Heterogeneous Agent Model," Working Papers - Economics wp2022_20.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.

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    More about this item

    Keywords

    agent-based models; estimation; forecasting;
    All these keywords.

    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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