Skip to main content

Advertisement

SpringerLink
Log in

The microfoundations of business cycles: an evolutionary, multi-agent model

  • Regular Article
  • Published:
Journal of Evolutionary Economics Aims and scope Submit manuscript

Abstract

This work presents an evolutionary model of output and investment dynamics yielding endogenous business cycles. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first one performs R&D and produces heterogeneous machine tools. Firms in the second industry invest in new machines and produce a homogenous consumption good. Consumers sell their labor and fully consume their income. In line with the empirical literature on investment patterns, we assume that firms’ investment decisions are lumpy and constrained by their financial structure. Simulation results show that the model is able to deliver self-sustaining patterns of growth characterized by the presence of endogenous business cycles. The model can also replicate the most important stylized facts concerning micro- and macro-economic dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. See, e.g., the sharp critique to the “representative agent fallacies” in Kirman (1989).

  2. See Caballero (1999).

  3. See Bottazzi and Secchi (2006). Castaldi and Dosi (2004) and Fagiolo et al. (2008) find that country output growth-rate distributions are also fat-tailed, over both cross-section and time-series dimensions.

  4. Different extrapolative expectation-formation rules based on both firm-specific past demand and aggregate market signal are explored in Dosi et al. (2006). Interestingly, one finds that increasing the computational sophistication of agents does not improve either the performance of the economy, as measured by average growth-rates, or the stability of growth patterns over time.

  5. We assume that there are no secondary markets for capital goods. Hence, firms have no incentives to reduce their capital stock.

  6. On the methodology of analysis of evolutionary / agent-based computational economics models, see e.g. Lane (1993) and Pyka and Fagiolo (2007).

  7. In Dosi et al. (2005, 2006) we perform extensive Monte Carlo sensitivity analyses on the most relevant parameters of the closest antecedent of the present model. The picture emerging from Monte Carlo studies confirms all results presented below.

  8. See Baxter and King (1999). Cf. also Dosi et al. (2005) for a discussion of the properties of alternative filtering techniques.

  9. We employ consumption-good firm sales as a proxy of firm size. Before pooling our data, we normalize each observation by the year-average of firm size in order to remove any time trends in our data. This allows us to get stationary size and growth distributions across years. Due to space constraints, we show the rank-size plot and the firm growth rate distribution plot for the work-or-die scenario only.

  10. Subbotin densities include as special cases the Normal (shape parameter β = 2) and the Laplace (β = 1) distributions. More on the application of the Subbotin family to the fitting of firm growth rates is in Bottazzi and Secchi (2006).

  11. All results refer to T = 600 time-periods, cf. Table 2. This econometric sample size is sufficient to allow for convergence of recursive moments of all statistics of interest.

  12. Note also that the ex post identity between savings and investments is always satisfied.

  13. Firm-productivity auto-correlations (up to lag 6) are computed by considering normalized productivity of firms that survived for at least 40 periods in the last 100 periods of any simulation run.

  14. For example, the demand side of the economy, as well as the labor market, are not explicitly modeled. A more detailed microfoundation of such admittedly neglected ingredients is one of the main points in our agenda.

References

  • Bartelsman E, Doms M (2000) Understanding productivity: lessons from longitudinal microdata. J Econ Lit 38:569–594

    Google Scholar 

  • Baxter M, King R (1999) Measuring business cycle: approximate band-pass filter for economic time series. Rev Econ Stat 81:575–593

    Article  Google Scholar 

  • Bottazzi G, Secchi A (2006) Explaining the distribution of firm growth rates. RAND J Econ 37:235–256

    Article  Google Scholar 

  • Brenner T, Werker C (2007) A taxonomy of inference in simulation models. Comput Econ 30:227–244

    Article  Google Scholar 

  • Caballero R (1999) Aggregate investment. In: Taylor J, Woodford M (eds) Handbook of macroeconomics. Elsevier Science, Amsterdam

    Google Scholar 

  • Castaldi C, Dosi G (2004) Income levels and income growth. Some new cross-country evidence and some interpretative puzzles. Working paper 2004/18, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy

  • Chiaromonte F, Dosi G (1993) Heterogeneity, competition, and macroeconomic dynamics. Struct Chang Econ Dyn 4:39–63

    Article  Google Scholar 

  • Dosi G (2005) Statistical regularities in the evolution of industries. A guide through some evidence and challenges for the theory. Working paper 2005/17, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy

  • Dosi G, Fabiani S, Aversi R, Meacci M (1994) The dynamics of international differentiation: a multi-country evolutionary model. Ind Corp Change 3:225–242

    Article  Google Scholar 

  • Dosi G, Fagiolo G, Roventini A (2005) Animal spirits, lumpy investment and endogenous business cycles. Working paper 2005/04, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy

  • Dosi G, Fagiolo G, Roventini A (2006) An evolutionary model of endogenous business cycles. Comput Econ 27:3–34

    Article  Google Scholar 

  • Dosi G, Freeman C, Fabiani S (1994) The process of economic development: introducing some stylized facts and theories on technologies, firms and institutions. Ind Corp Change 3:1–45

    Article  Google Scholar 

  • Fagiolo G, Moneta A, Windrum P (2007) A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Comput Econ 30:195–226

    Article  Google Scholar 

  • Fagiolo G, Napoletano M, Roventini A (2008) Are output growth-rate distributions fat-tailed? Some evidence from OECD countries. J Appl Econ (in press)

  • Hubbard R (1998) Capital-market imperfections and investment. J Econ Lit 36:193–225

    Google Scholar 

  • King R, Rebelo S (1999) Resuscitating real business cycles. In: Taylor J, Woodford M (eds) Handbook of macroeconomics. Elsevier Science, Amsterdam

    Google Scholar 

  • Kirman, A (1989) The intrinsic limits of modern economic theory: the emperor has no clothes. Econ J 99:126–139

    Article  Google Scholar 

  • Lane DA (1993) Artificial worlds and economics, part I and II. J Evol Econ 3:89–107 and 177–197

    Article  Google Scholar 

  • Mankiw GN, Romer D (eds) (1991) New Keynesian economics. MIT, Cambridge

    Google Scholar 

  • Napoletano M, Roventini A, Sapio S (2006) Are business cycles all alike? A bandpass filter analysis of the Italian and US cycles. Riv Ital Econ 1:87–118

    Google Scholar 

  • Nelson R, Winter S (1982) An evolutionary theory of economic change. The Belknap Press of Harvard University Press, Cambridge

    Google Scholar 

  • Pyka A, Fagiolo G (2007) Agent-based modelling: a methodology for neo-schumpeterian economics. In: Hanusch H, Pyka A (eds) The Elgar companion to neo-Schumpeterian economics. Edward Elgar, Cheltenham

    Google Scholar 

  • Stock J, Watson M (1999) Business cycle fluctuations in U.S. macroeconomic time series. In: Taylor J, Woodford M (eds) Handbook of macroeconomics. Elsevier Science, Amsterdam

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sant’Anna School of Advanced Studies, Pisa, Italy

    Giovanni Dosi & Andrea Roventini

  2. Laboratory of Economics and Management, Sant’Anna School of Advanced Studies, Piazza Martiri della Libertà, 56127, Pisa, Italy

    Giorgio Fagiolo

  3. University of Modena and Reggio Emilia, Modena, Italy

    Andrea Roventini

Authors
  1. Giovanni Dosi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giorgio Fagiolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Roventini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giorgio Fagiolo.

Additional information

Thanks to Giulio Bottazzi, Andrea Ginzburg, Marco Lippi, Marco Mazzoli, Mauro Napoletano and Sandro Sapio. All usual disclaimers apply.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dosi, G., Fagiolo, G. & Roventini, A. The microfoundations of business cycles: an evolutionary, multi-agent model. J Evol Econ 18, 413–432 (2008). https://doi.org/10.1007/s00191-008-0094-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00191-008-0094-8

Keywords

JEL Classification

Search

Navigation