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

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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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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Barde, Sylvain, 2024. "Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
    2. 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).
    3. Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized Neural Networks for Agent-Based Model Forecasting," Bank of Russia Working Paper Series wps115, Bank of Russia.
    4. 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.
    5. 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.
    6. 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).
    7. Samuel Wiese & Jagoda Kaszowska-Mojsa & Joel Dyer & Jose Moran & Marco Pangallo & Francois Lafond & John Muellbauer & Anisoara Calinescu & J. Doyne Farmer, 2024. "Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model," Papers 2409.18760, arXiv.org.
    8. Simone Brusatin & Tommaso Padoan & Andrea Coletta & Domenico Delli Gatti & Aldo Glielmo, 2024. "Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling," Papers 2405.02161, arXiv.org, revised Oct 2024.
    9. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Bank of Russia Working Paper Series wps104, Bank of Russia.
    10. 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.
    11. 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.
    12. Lux, Thomas, 2024. "Lack of identification of parameters in a simple behavioral macroeconomic model," Economics Working Papers 2024-02, Christian-Albrechts-University of Kiel, Department of Economics.
    13. 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.
    14. 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.
    15. 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.
    16. Jlenia Di Noia, 2024. "When firms buy corporate bonds: an agent-based approach to credit within firms," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(4), pages 689-725, October.
    17. 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.
    18. 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.
    19. 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.
    20. 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).
    21. 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.
    22. 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.
    23. Domenico Delli Gatti & Severin Reissl, 2020. "ABC: An Agent Based Exploration of the Macroeconomic Effects of Covid-19," CESifo Working Paper Series 8763, CESifo.
    24. 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.

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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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