Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model

Popoyan, Lilit; Napoletano, Mauro; Roventini, Andrea

doi:10.1016/j.jebo.2016.12.017

Cited by 90 publications

(21 citation statements)

References 67 publications

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“…Such developments are especially important for complex macroeconomic agent-based models (see e.g. Dosi et al, 2010Dosi et al, , 2013Dosi et al, , 2015Dosi et al, , 2017aPopoyan et al, 2017) as they could allow the development of a standardized and robust procedure for model calibration and validation, thus closing the existing gap with Dynamics…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Agent-Based Model Calibration Using Machine Learning Surrogates

2017

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Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large outof-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces. KEY WORDSAgent based model; calibration; machine learning; surrogate; meta-model. JELC15, C52, C63.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Agent-Based Model Calibration Using Machine Learning Surrogates

2017

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“…Our model's specification is mostly based on Ashraf et al (2017) and Popoyan et al (2017). We also add several simplified elements from Dosi et al (2010) to model endogenous productivity growth.…”

Section: Model Set-upmentioning

confidence: 99%

What Do Aggregate Saving Rates (Not) Show?

Ponomarenko

2017

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe aggregate saving indicator does not directly reflect changes in individuals' microeconomic behavior. From the official statistics' point of view, households choosebetween spending, which generates additional income and consumption in the economy, and setting money aside, which does not. Formally, households may not (if the authors disregard housing investment) choose to save, because the aggregate saving statistical indicator is a residual concept defined as the ensuing difference between aggregate disposable income and consumption. It measures the change in net worth, which, in a closed economy, may only be generated by the production of capital goods and an increase in inventories. Using an agentbased model, the authors show that shocks unrelated to structural changes in households' behavior may generate positively correlated fluctuations in the aggregate saving rate, productivity growth and lending. Meanwhile, a genuine increase in the average individual propensity to save is not necessarily associated with a higher aggregate saving rate.(Published in Special Issue Agent-based modelling and complexity economics) JEL C63 G21 O16 O40

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“…More generally, there appears to be strong interactions not only between fiscal and monetary policies but also between macroprudential and monetary ones (more on that in Popoyan et al, 2015, and the papers presented in Section 6.3). Finally, the effects of monetary policies become sharper as the level of income inequality increases (see also Dosi et al, 2013).…”

Section: Monetary Policymentioning

confidence: 99%

Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead

Fagiolo

Roventini

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe Great Recession seems to be a natural experiment for economic analysis, in that it has shown the inadequacy of the predominant theoretical framework -the New Neoclassical Synthesis (NNS) -grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue should escape the strong theoretical requirements of NNS models (e.g., equilibrium, rationality, representative agent, etc.) and consider the economy as a complex evolving system, i.e. as an ecology populated by heterogenous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This is indeed the methodological core of agent-based computational economics (ACE), which is presented in this paper. We also discuss how ACE has been applied to policy analysis issues, and we provide a survey of macroeconomic policy applications (fiscal and monetary policy, bank regulation, labor market structural reforms and climate change interventions). Finally, we conclude by discussing the methodological status of ACE, as well as the problems it raises.

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Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model

Cited by 90 publications

References 67 publications

Agent-Based Model Calibration Using Machine Learning Surrogates

Agent-Based Model Calibration Using Machine Learning Surrogates

What Do Aggregate Saving Rates (Not) Show?

Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead

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