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Monetary policy and large crises in a financial accelerator agent-based model

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  • Giri, Federico
  • Riccetti, Luca
  • Russo, Alberto
  • Gallegati, Mauro

Abstract

An accommodating monetary policy followed by a sudden increase of the short term interest rate often leads to a bubble burst and to an economic slowdown. Through the implementation of an Agent Based Model with a financial accelerator mechanism we are able to study the relationship between monetary policy and large-scale crisis events. A two-step computational approach is proposed which performs (i) a pattern search of “double dip” episodes and (ii) counter-factual simulations implementing unconventional monetary policy. The main results can be summarized as follow: a) sudden and sharp increases of the policy rate can generate recessions; b) after a crisis, returning too soon and too quickly to a normal monetary policy regime can generate a “double dip” recession, while c) keeping the short term interest rate anchored to the zero lower bound in the short run can successfully avoid a further slowdown.

Suggested Citation

  • Giri, Federico & Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2019. "Monetary policy and large crises in a financial accelerator agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 42-58.
  • Handle: RePEc:eee:jeborg:v:157:y:2019:i:c:p:42-58
    DOI: 10.1016/j.jebo.2018.04.007
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    2. Eric Guerci & Nobuyuki Hanaki & Mauro Napoletano, 2019. "Introduction to special issue on “Complex evolving system approach to market dynamics and policy design”," Sciences Po publications info:hdl:2441/4cb1o2msej8, Sciences Po.
    3. Hamed Ghiaie, 2018. "Shadow Bank run, Housing and Credit Market: The Story of a Recession," THEMA Working Papers 2018-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    4. Papadopoulos, Georgios, 2020. "Probing the mechanism: lending rate setting in a data-driven agent-based model," MPRA Paper 102749, University Library of Munich, Germany.
    5. Giovanni Dosi & Andrea Roventini, 2017. "Agent-Based Macroeconomics and Classical Political Economy: Some Italian Roots," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 261-283, November.
    6. Ghiaie Hamed, 2020. "Shadow Bank Run, Housing and Credit Market: The Story of a Recession," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(2), pages 1-30, June.
    7. Gobbi, Alessandro & Grazzini, Jakob, 2019. "A basic New Keynesian DSGE model with dispersed information: An agent-based approach," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 101-116.
    8. Ermanno Catullo & Mauro Gallegati & Alberto Russo, 2020. "Forecasting in a complex environment: Machine learning sales expectations in a Stock Flow Consistent Agent-Based simulation model," Working Papers 2020/17, Economics Department, Universitat Jaume I, Castellón (Spain).

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    More about this item

    Keywords

    Monetary policy; Large crises; Agent based model; Financial accelerator; Zero lower bound;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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