Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access September 24, 2010

Credit Money and Macroeconomic Instability in the Agent-based Model and Simulator Eurace

  • Silvano Cincotti EMAIL logo , Marco Raberto and Andrea Teglio
From the journal Economics

Abstract

This paper investigates the interplay between monetary aggregates and the dynamics and variability of output and prices by considering both the money supplied by commercial banks as credit to firms and the fiat money created by the central bank through the quantitative easing monetary policy. The authors address this problem by means of an agent-based model and simulator, called Eurace, which is characterized by a complete set of interrelated markets and different types of interacting agents, modeled according to a rigorous balance-sheet approach. The dynamics of credit money is endogenous and depends on the supply of credit from the banking system, which is constrained by its equity base, and the demand of credit from firms in order to finance their production activity. Alternative dynamic paths for credit money have been produced by setting different firms’ dividend policies. Results point out the strict dependence of output and prices dynamics on monetary aggregates, and show the emergence of endogenous business cycles which are mainly due to the interplay between the real economic activity and its financing through the credit market. In particular, the amplitude of the business cycles strongly rises when the fraction of earnings paid out by firms as dividends is higher, that is when firms are more constrained to borrow credit money to fund their activity. This interesting evidence can be explained by the fact that the level of firms leverage, defined as the debt-equity ratio, can be considered ad a proxy of the likelihood of bankruptcy, an event which causes mass layoffs and supply decrease.

JEL Classification: E42; E2; E32

References

Basu, N., Pryor, R., and Quint, T. (1998). ASPEN: a microsimulation model of the economy. Computational Economics, 12(3): 223–241. urlhttp://ideas.repec.org/a/kap/compec/v12y1998i3p223-41.html.Search in Google Scholar

Benartzi, S., and Thaler, R. H. (1995). Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics, 110(1): 73–92. urlhttp://ideas.repec.org/a/tpr/qjecon/v110y1995i1p73-92.html.Search in Google Scholar

Bernanke, S. (2004). The great moderation. Federal Reserve Board: Speech at the meetings of the Eastern Economic Association, Washington, DC February 20, 2004. urlhttp://www.federalreserve.gov/boarddocs/speeches/2004/20040220/default.htm.Search in Google Scholar

Bruun, C. (1999). Agent-based Keynesian economics: simulating a monetary production system bottom-up. University of Aaborg. urlhttp://vbn.aau.dk/files/93137/35111999_1.pdf.Search in Google Scholar

Carroll, C. D. (2001). A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives, 15(3): 23–45. urlhttp://ideas.repec.org/a/aea/jecper/v15y2001i3p23-45.html.10.3386/w8387Search in Google Scholar

Clarida, R., Gali, J., and Gertler, M. (1999). The science of monetary policy: A new keynesian perspective. Journal of Economic Literature, 37(4): 1661–1707. urlhttp://links.jstor.org/sici?sici=0022-0515%28199912%2937%3A4%3C1661%3ATSOMPA%3E2.0.CO%3B2-K.Search in Google Scholar

D5.1 (2007). Agent based models of goods, labour and credit markets. Eurace Deliverable D5.1, Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

D5.2 (2008). Computational experiments of policy design on goods, labour and-credit markets. Eurace Deliverable D5.2, Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

D6.1 (2007). Agent based models of financial markets. Eurace Deliverable D6.1, Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

D6.2 (2008). Computational experiments of policy design on financial markets. Eurace Deliverable D6.2, Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

D7.1 (2007). Agent based models for skill dynamics and innovation. Eurace Deliverable D7.1, Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

D7.2 (2008). Computational experiments of policy design on skill dynamics and innovation. Discussion paper, Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

Dawid, H., Gemkow, S., Harting, P., Kabus, K., Neugart, M., and Wersching, K. (2008). Skills, innovation, and growth: An agent-based policy analysis. Journal of Economics and Statistics, 228(2+3): 251–275. urlhttp://ideas.repec.org/a/jns/jbstat/v228y2008i2+3p251-275.html.Search in Google Scholar

Dawid, H., Gemkow, S., Harting, P., and Neugart, M. (2009). On the effects of skill upgrading in the presence of spatial labor market frictions: An agent-based analysis of spatial policy design. Journal of Artificial Societies and Social Simulation, 12(4). urlhttp://ideas.repec.org/a/jas/jasssj/2009-65-1.html.Search in Google Scholar

Deaton, A. (1992). Household saving in LDCs: Credit markets, insurance and welfare. The Scandinavian Journal of Economics, 94(2): 253–273. urlhttp://ideas.repec.org/a/bla/scandj/v94y1992i2p253-73.html.Search in Google Scholar

Delli Gatti, D., Gallegati, M., Greenwald, B., Russo, A., and Stiglitz, J. (2009). Business fluctuations and bankruptcy avalanches in an evolving network economy. Journal of Economic Interaction and Coordination, 4(2): 195–212. urlhttp://ideas.repec.org/a/spr/jeicoo/v4y2009i2p195-212.html.Search in Google Scholar

Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5): 41–60. urlhttp://www-users.cs.umn.edu/~ketter/teaching/MASRW/2009/resources/papers/Epstein1999.pdf.Search in Google Scholar

Epstein, J. M., and Axtell, R. L. (1996). Growing artificial societies: social science from the bottom up. Washington, DC: Brookings Institution.10.7551/mitpress/3374.001.0001Search in Google Scholar

Eurace, P. C. (2009). EURACE Final Activity Report. Eurace Project Consortium. urlhttp://www.eurace.org.Search in Google Scholar

Fisher, I. (1933). The debt-deflation theory of great depressions. Econometrica, 1(4): 337–357. urlhttp://fraser.stlouisfed.org/docs/meltzer/fisdeb33.pdf.Search in Google Scholar

Friedman, M. (1953). The case for flexible exchange rates. Chicago: University of Chicago Press.Search in Google Scholar

Graziani, A. (2003). The monetary theory of production. Cambridge: Cambridge University Press.10.1017/CBO9780511493546Search in Google Scholar

Hicks, J. R. (1937). Mr Keynes and the classics; a suggested interpretation. Econometrica, 5(2): 147–159. urlhttp://stevereads.com/papers_to_read/keynes_and_the_classics.pdf.Search in Google Scholar

Kahneman, D., and Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2): 263–292. urlhttp://ideas.repec.org/a/ecm/emetrp/v47y1979i2p263-91.html.Search in Google Scholar

Kutschinski, E., Uthmann, T., and Polani, D. (2003). Learning competitive pricing strategies by multi-agent reinforcement learning. Journal of Economic Dynamics and Control, 27(11-12): 2207–2218. urlhttp://ideas.repec.org/a/eee/dyncon/v27y2003i11-12p2207-2218.html.Search in Google Scholar

LeBaron, B. (2006). Agent-based computational finance. In L. Tesfatsion, and K. Judd (Eds.), Handbook of computational economics, Volume 2: Agent-based computational economics. Amsterdam: North-Holland.Search in Google Scholar

Minsky, H. (1986). Stabilizing an unstable economy. New Haven, CT: Yale University Press.Search in Google Scholar

Raberto, M., Teglio, A., and Cincotti, S. (2006). A dynamic general disequilibrium model of a sequential monetary production economy. Chaos, Solitons and Fractals, 29(3): 566–577.Search in Google Scholar

Raberto, M., Teglio, A., and Cincotti, S. (2008). Integrating real and financial markets in an agent-based economic model: An application to monetary policy design. Computational Economics, 32(1-2): 147–162. urlhttp://portal.acm.org/citation.cfm?id=1394922&dl=GUIDE&coll=GUIDE&CFID=80818047&CFTOKEN=86287954.Search in Google Scholar

Sallans, B., Pfister, A., Karatzoglou, A., and Dorffner, G. (2003). Simulations and validation of an integrated markets model. Journal of Artificial Societies and Social Simulation, 6(4). urlhttp://ideas.repec.org/a/jas/jasssj/2003-5-2.html.Search in Google Scholar

Tassier, T. (2001). Emerging small-world referral networks in evolutionary labor markets. IEEE Transaction of Evolutionary Computation, 5(5): 482–492. urlhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00956712&tag=1.Search in Google Scholar

Teglio, A., Raberto, M., and Cincotti, S. (2009). Explaining equity excess return by means of an agent-based financial market. In M. Beckmann, H. P. Künzi, G. Fandel, W. Trockel, A. Basile, A. Drexl, H. Dawid, K. Inderfurth, W. Kürsten, U. Schittko, C. Hernández, M. Posada, and A. López-Paredes (Eds.), Artificial Economics, volume 631 of Lecture Notes in Economics and Mathematical Systems. Berlin: Springer.Search in Google Scholar

Tesfatsion, L. (2001). Structure, behaviour, and market power in an evolutionary labour market with adaptive search. Journal of Economics Dynamics and Control, 25: 419–457. urlhttp://ideas.repec.org/a/eee/dyncon/v25y2001i3-4p419-457.html.Search in Google Scholar

Tesfatsion, L., and Judd, K. (Eds.) (2006). Handbook of computational economics, Volume 2: Agent-based computational economics. Amsterdam: North-Holland.Search in Google Scholar

Tversky, A., and Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4): 297–323. urlhttp://ideas.repec.org/a/kap/jrisku/v5y1992i4p297-323.html.Search in Google Scholar

Published Online: 2010-09-24
Published in Print: 2010-12-01

© 2010 Silvano Cincotti et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Downloaded on 28.3.2024 from https://www.degruyter.com/document/doi/10.5018/economics-ejournal.ja.2010-26/html
Scroll to top button