Monetary Policy and Large Crises in a Financial Accelerator Agent-Based Model

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. Two examples are the Great Depression of 1929 and the Great Recession of 2008. 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. 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.

Does too much liquidity generate instability?

“…An accommodating monetary policy followed by a sudden increase in the shortterm interest rate often leads to a bubble burst and to an economic slowdown. In this vein, Giri et al (2019) show that sudden and sharp increases of the policy rate can generate recessions, while keeping the short term interest rate anchored to the zero lower bound in the short run can successfully avoid a further slowdown.…”

Forecasting in a complex environment: Machine learning sales expectations in a stock flow consistent agent-based simulation model

“…Also in the case of our model, agents (namely firms) are able to detect both a positive and a negative growth trend. role of wage flexibility Dosi et al (2017) , Riccetti et al (2015) provide a fully decentralized matching protocol for simulating the working of different markets in an agent-based macro setting, then employed to analyze inequality and financial crises , financial regulation , monetary policy at the Zero Lower Bound ( Caiani et al, 2016;Giri et al, 2019 ) propose a benchmark agent-based stock-flow consistent macro model, then extended to explore the interplay between inequality and long-run growth ( Caiani et al, 2019b;2020; present the largescale EURACE model, and discuss some general aspects of ABM, a framework already used for analysing financial regulation ( Cincotti et al, 2010 ), regional gaps and innovation policy ( Dawid et al, 2014 ), and fiscal policy ( Teglio et al, 2019 ); see Dawid and Delli Gatti (2018) , for a comprehensive review. 3 For the next future, there are two main challenges regarding macro agent-based models: enriching the modeling of agents' behavior in order to develop a new framework with heterogeneous boundedly rational expectations (here an integration between computational and experimental economics is in order); improve their empirical performance by developing new techniques for calibrating and estimating macro agentbased models (just to make a few examples: Bargigli et al, 2014;Bargigli et al, 2018;Bianchi et al, 2007;Grazzini et al, 2017 ).…”

Energy Price Shocks and Stabilization Policies in a Multi-Agent Macroeconomic Model for the Euro Area

“…Policy analyses have been extensively explored in the macroeconomic ABM literature (Fagiolo and Roventini, 2012;Dawid and Delli Gatti, 2018), focusing on different policy areas such as fiscal (Dosi et al, 2013;Teglio et al, 2019), monetary (Delli Gatti and Desiderio, 2015;Giri et al, 2019), labor market (Dosi et al, 2018), macro-prudential (Aldasoro et al, 2017;Popoyan et al, 2017) and regional policies as well as virus containment interventions and vaccination strategies (Basurto et al, 2021;Delli Gatti et al, 2021). More recently, the potential of agent-based modelling for performing economic and policy analyses related to climate and energy issues has been increasingly recognized (Balint et al, 2017;Hansen et al, 2019;Castro et al, 2020).…”