Economic convergence: Policy implications from a heterogeneous agent model

Dawid, Herbert; Harting, Philipp; Neugart, Michael

doi:10.1016/j.jedc.2014.04.004

Cited by 113 publications

(71 citation statements)

References 38 publications

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“…Indeed, the effects of the adoption of the complete Basel III regulation are much stronger than the summation of the impact of its single components. In addition, the levers of Basel III are non-additive: the inclusion of additional components does not always 2 For germane macroeconomic agent-based models with credit and financial markets, see Delli Gatti et al (2005Gatti et al ( , 2010, Ashraf et al (2011), Gai et al (2011), Battiston et al (2012, Geanakoplos et al (2012), Raberto et al (2012Raberto et al ( , 2014, Dosi et al (2010Dosi et al ( , 2013Dosi et al ( , 2015, Riccetti et al (2013), Dawid et al (2014), Krug et al (2015), Napoletano et al (2015), Seppecher & Salle (2015), van der Hoog & Dawid (2015), van der Hoog (2015), and the papers in Gaffard & Napoletano (2012). See Fagiolo & Roventini (2012) for a critical comparison of macroeconomic policies in standard DSGE and agent-based models.…”

Section: Introductionmentioning

confidence: 99%

Taming Macroeconomic Instability: Monetary and Macro Prudential Policy Interactions in an Agent-Based Model

2016

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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 AbstractWe develop an agent-based model to study the macroeconomic impact of alternative macro prudential regulations and their possible interactions with different monetary policy rules. The aim is to shed light on the most appropriate policy mix to achieve the resilience of the banking sector and foster macroeconomic stability. Simulation results show that a triplemandate Taylor rule, focused on output gap, inflation and credit growth, and a Basel III prudential regulation is the best policy mix to improve the stability of the banking sector and smooth output fluctuations. Moreover, we consider the different levers of Basel III and their combinations. We find that minimum capital requirements and counter-cyclical capital buffers allow to achieve results close to the Basel III first-best with a much more simplified regulatory framework. Finally, the components of Basel III are non-additive: the inclusion of an additional lever does not always improve the performance of the macro prudential regulation.

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Section: Introductionmentioning

confidence: 99%

Taming Macroeconomic Instability: Monetary and Macro Prudential Policy Interactions in an Agent-Based Model

2016

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“…In particular, they study the impact of policies aimed at improving workers skills and firms' technological adoption on innovation, commuting flows, inequality dynamics and economic convergence. Simulation results show that both policies are complementary and that human capital policies foster regional cohesion only if labor markets are separated (Dawid et al, 2014b). Moreover, the effects of policies depends on the flexibility of the labor markets (Dawid et al, 2014a).…”

Section: Labor Market Policymentioning

confidence: 97%

“…In a series of papers, Dawid et al (2012aDawid et al ( , 2014b employ the Eurace@Unibi model to analyze the convergence of regions characterized by local labor markets where workers have heterogenous skills. In particular, they study the impact of policies aimed at improving workers skills and firms' technological adoption on innovation, commuting flows, inequality dynamics and economic convergence.…”

Section: Labor Market 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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“…As stated by Chen et al (2012), 5 See e.g. Dosi et al (2010Dosi et al ( , 2013Dosi et al ( , 2015; Caiani et al (2016); Assenza et al (2015) and Dawid et al (2014a) on business cycle dynamics, Lamperti et al (2017) on growth, green transitions and climate change, Dawid et al (2014b) on regional convergence and Leal et al (2014) on financial markets. The surveys in Roventini (2012, 2017) provides a more exhaustive list.…”

Section: Calibration and Validation Of Agent-based Models: The Case Fmentioning

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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Economic convergence: Policy implications from a heterogeneous agent model

Cited by 113 publications

References 38 publications

Taming Macroeconomic Instability: Monetary and Macro Prudential Policy Interactions in an Agent-Based Model

Taming Macroeconomic Instability: Monetary and Macro Prudential Policy Interactions in an Agent-Based Model

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

Agent-Based Model Calibration Using Machine Learning Surrogates

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