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On the scientific status of economic policy: a tale of alternative paradigms*

Published online by Cambridge University Press:  26 April 2012

Giorgio Fagiolo  and
Andrea Roventini
Giorgio Fagiolo*
Affiliation:
Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, I-56127 Pisa, Italy; e-mail: giorgio.fagiolo@sssup.it
Andrea Roventini*
Affiliation:
Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, I-56127 Pisa, Italy; e-mail: giorgio.fagiolo@sssup.it Dipartimento di Scienze Economiche, Università di Verona, viale dell'Università 3, I-37129 Verona, Italy; e-mail: andrea.roventini@univr.it University Paris Quest Nanterre La Defense, France OFCE, Sophia-Antipolis, France
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Abstract

In recent years, a number of contributions have argued that monetary—and, more generally, economic—policy is finally becoming more of a science. According to these authors, policy rules implemented by central banks are nowadays well supported by a theoretical framework (the New Neoclassical Synthesis) upon which a general consensus has emerged in the economic profession. In other words, scientific discussion on economic policy seems to be ultimately confined to either fine-tuning this ‘consensus’ model, or assessing the extent to which ‘elements of art’ still exist in the conduct of monetary policy. In this paper, we present a substantially opposite view, rooted in a critical discussion of the theoretical, empirical, and political-economy pitfalls of the neoclassical approach to policy analysis. Our discussion indicates that we are still far from building a science of economic policy. We suggest that a more fruitful research avenue to pursue is to explore alternative theoretical paradigms, which can escape the strong theoretical requirements of neoclassical models (e.g. equilibrium, rationality, etc.). We briefly introduce one of the most successful alternative research projects—known in the literature as agent-based computational economics (ACE)—and we present the way it has been applied to policy analysis issues. We conclude by discussing the methodological status of ACE, as well as the (many) problems it raises.

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The Knowledge Engineering Review , Volume 27 , Special Issue 2: Agent-Based Computational Economics , 26 April 2012 , pp. 163 - 185
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Copyright © Cambridge University Press 2012

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References

Aghion, P., Howitt, P. 2007. Appropriate growth policy: a unifying framework. Journal of the European Economic Association 4, 269–314.CrossRefGoogle Scholar
Akerlof, G. A. 2007. The missing motivation in macroeconomics. American Economic Review 97, 5–36.CrossRefGoogle Scholar
Albert, R., Barabasi, A. L. 2002. Statistical mechanics of complex networks. Reviews of Modern Physics 4, 47–97.CrossRefGoogle Scholar
Alessi, L., Barigozzi, M., Capasso, M. 2007. A Review of Nonfundamentalness and Identification in Structural VAR Models. Working Paper 2007/22, Laboratory of Economics and Management (LEM).CrossRefGoogle Scholar
Aoki, M. 2006. Not more so: some concepts outside the DSGE framework. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Benhabib, J., Schmitt-Grohé, S., Uribe, M. 2001. The perils of Taylor rules. Journal of Economic Theory 96, 40–69.CrossRefGoogle Scholar
Beyer, A., Farmer, R. E. A. 2004. On the Indeterminacy of New-Keynesian Economics. Working Paper Series No. 323, European Central Bank, Frankfurt, Germany.CrossRefGoogle Scholar
Brock, W. A. 1999. Scaling in economics: a reader's guide. Industrial and Corporate Change 8, 409–446.CrossRefGoogle Scholar
Brock, W. A., Durlauf, S. N. 2001. Interactions-based models. In Handbook of Econometrics, Heckman, J. & Leamer, E. (eds). North Holland, 5.Google Scholar
Brock, W. A., Durlauf, S., Nason, J. M., Rondina, G. 2007. Simple versus optimal rules as guides to policy. Journal of Monetary Economics 54, 1372–1396.CrossRefGoogle Scholar
Calvo, G. A. 1983. Staggered prices in a utility-maximizing framework. Journal of Monetary Economics 12, 383–398.CrossRefGoogle Scholar
Canova, F. 2008. How much structure in empirical models? In Palgrave Handbook of Econometrics, Mills, T. & Patterson, K. (eds). Applied Econometrics. Palgrave Macmillan, 2.Google Scholar
Canova, F., Sala, L. 2005. Back to Square One: Identification Issues in DSGE Models. Working Paper Series No. 583. European Central Bank, Frankfurt, Germany.CrossRefGoogle Scholar
Carayol, N., Roux, P., Yildizoglu, M. 2008. Inefficiencies in a model of spatial networks formation with positive externalities. Journal of Economic Behavior and Organization 67, 495–511.CrossRefGoogle Scholar
Chen, S.-H., Chie, B.-T. 2008. Lottery markets design, micro structure and macro behavior: an ACE approach. Journal of Economic Behavior and Organization 67, 463–480.CrossRefGoogle Scholar
Christiano, L. G., Eichenbaum, M., Evans, C. L. 2005. Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113, 1–45.CrossRefGoogle Scholar
Clarida, R., Galí, J., Gertler, M. 1999. The science of monetary policy: a new Keynesian perspective. Journal of Economic Literature 37, 1661–1707.CrossRefGoogle Scholar
Cogley, T., Nason, J. M. 1993. Impulse dynamics and propagation mechanisms in a real business cycle model. Economic Letters 43, 77–81.CrossRefGoogle Scholar
Colander, D. 2005. The future of economics: the appropriately educated in pursuit of the knowable. Cambridge Journal of Economics 29, 927–941.CrossRefGoogle Scholar
Colander, D. 2006a. Introduction. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.CrossRefGoogle Scholar
Colander, D. 2006b. Post walrasian macroeconomics: some historic links. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.CrossRefGoogle Scholar
Colander, D. (ed.) 2006c. Post Walrasian Macroeconomics. Cambridge University Press.CrossRefGoogle Scholar
Dawid, H., Fagiolo, G. (eds) 2008. Special issue on “Agent-based models for economic policy design”. Journal of Economic Behavior and Organization 67, 351–544.CrossRefGoogle Scholar
Dawid, H., Gemkow, S., Harting, P., Kabus, K., Neugart, M., Wersching, K. 2008. Skills, innovation, and growth: an agent-based policy analysis. Journal of Economics and Statistics 228, 251–275.Google Scholar
Debreu, G. 1974. Excess demand function. Journal of Mathematical Economics 1, 15–23.CrossRefGoogle Scholar
Dixit, A., Stiglitz, J. 1977. Monopolistic competition and optimum product diversity. American Economic Review 67, 297–308.Google Scholar
Dosi, G., Egidi, M. 1991. Substantive and procedural uncertainty: an exploration of economic behaviours in changing environments. Journal of Evolutionary Economics 1, 145–168.CrossRefGoogle Scholar
Dosi, G., Nelson, R. 1994. An introduction to evolutionary theories in economics. Journal of Evolutionary Economics 4, 153–172.CrossRefGoogle Scholar
Dosi, G., Fagiolo, G., Roventini, A. 2006. An evolutionary model of endogenous business cycles. Computational Economics 27, 3–34.CrossRefGoogle Scholar
Dosi, G., Fagiolo, G., Roventini, A. 2010. Schumpeter meeting keynes: a policy-friendly model of endogenous growth and business cycles. Journal of Economic Dynamics & Control 34, 1748–1767.CrossRefGoogle Scholar
Dosi, G., Marengo, L., Fagiolo, G. 2005. Learning in evolutionary environment. In Evolutionary Principles of Economics, Dopfer, K. (ed.). Cambridge University Press.Google Scholar
Duffy, J., Unver, M. 2008. Internet auctions with artificial adaptive agents: a study on market design. Journal of Economic Behavior and Organization 67, 394–417.CrossRefGoogle Scholar
Fagiolo, G., Birchenhall, C., Windrum, P. (eds) 2007. Special issue on “Empirical validation in agent-based models”. Computational Economics 30(3).CrossRefGoogle 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. Computational Economics 30, 195–226.CrossRefGoogle Scholar
Fagiolo, G., Napoletano, M., Roventini, A. 2008. Are output growth-rate distributions fat-tailed? Some evidence from OECD countries. Journal of Applied Econometrics 23, 639–669.CrossRefGoogle Scholar
Favero, C. 2007. Model Evaluation in Macroeconometrics: From Early Empirical Macroeconomic Models to DSGE Models. Working paper 327. IGIER, Bocconi University, Milan, Italy.Google Scholar
Fernandez-Villaverde, J., Rubio-Ramirez, J. F., Sargent, T. 2006. A, B, C's, (and D's) for understanding VARs. Journal of the European Economic Association 4, 466–474.Google Scholar
Forni, M., Lippi, M. 1997. Aggregation and the Microfoundations of Dynamic Macroeconomics. Oxford University Press.CrossRefGoogle Scholar
Forni, M., Lippi, M. 1999. Aggregation of linear dynamic microeconomic models. Journal of Mathematical Economics 31, 131–158.CrossRefGoogle Scholar
Friedman, M. 1953. The methodology of positive economics. In Essays in Positive Economics, Friedman, M. (ed.). University of Chicago Press.Google Scholar
Fukac, M., Pagan, A. 2006. Issues in Adopting DSGE Models for Use in the Policy Process. Working paper 10/2006. CAMA.Google Scholar
Fukuyama, F. 1992. The End of History and the Last Man. Penguin.Google Scholar
Galí, J. 2008. Monetary Policy, Inflation, and the Business Cycle. Princeton University Press.Google Scholar
Galí, J., Gertler, M. 2007. Macroeconomic modelling for monetary policy evaluation. Journal of Economic Perspectives 21, 25–46.CrossRefGoogle Scholar
Giannoni, M. P., Woodford, M. 2002a. Optimal Interest-rate Rules: I. General Theory. Working Paper 9419. NBER.CrossRefGoogle Scholar
Giannoni, M. P., Woodford, M. 2002b. Optimal Interest-rate Rules: II. Applications. Working Paper 9420. NBER.CrossRefGoogle Scholar
Goodfriend, M. 2007. How the world achieved consensus on monetary policy. Journal of Economic Perspectives 21, 47–68.CrossRefGoogle Scholar
Goodfriend, M., King, R. 1997. The new neoclassical synthesis and the role of monetary policy. NBER Macroeconomics Annual 12, 231–282.CrossRefGoogle Scholar
Greenspan, A. 2004. Risk and uncertainty in monetary policy. The American Economic Review 94, 33–40.CrossRefGoogle Scholar
Happe, K., Balmann, A., Kellermann, K., Sahrbacher, C. 2008. Does structure matter? The impact of switching the agricultural policy regime on farm structures. Journal of Economic Behavior and Organization 67, 431–444.CrossRefGoogle Scholar
Hoover, K. D., Johansen, S., Juselius, K. 2008. Allowing the data to speak freely: the macroeconometrics of the cointegrated vector autoregression. American Economic Review 98, 251–255.CrossRefGoogle Scholar
Howitt, P. 2006. Monetary policy and the limitations of economic knowledge. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Johansen, S. 2006. Confronting the economic model with the data. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Johansen, S., Juselius, K. 2006. Extracting information from the data: a European view on empirical macro. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Juselius, K., Franchi, M. 2007. Taking a DSGE model to the data meaningfully. Economics: the open-access, open-assessment E-Journal, 1(2007-4). http://dx.doi.org/10.5018/economics-ejournal.ja.2007-4CrossRefGoogle Scholar
Kahneman, D., Tversky, A. (eds) 2000. Choices, Values, and Frames. Cambridge University Press.CrossRefGoogle Scholar
King, R., Rebelo, S. 1999. Resuscitating real business cycles. In Handbook of Macroeoconomics, Taylor, J. & Woodford, M. (eds). Elsevier Science.Google Scholar
Kirman, A. P. 1989. The intrinsic limits of modern economic theory: the emperor has no clothes. Economic Journal 99, 126–139.CrossRefGoogle Scholar
Kirman, A. P. 1992. Whom or what does the representative individual represent? Journal of Economic Perspectives 6, 117–136.CrossRefGoogle Scholar
Kirman, A. P., Koch, K. 1986. Market excess demand functions: identical preferences and collinear endowments. Review of Economic Studies 53, 457–463.CrossRefGoogle Scholar
Knight, F. 1921. Risk, Uncertainty, and Profits. Chicago University Press.Google Scholar
Lane, D., Maxfield, R. 2004. Ontological uncertainty and innovation. Journal of Evolutionary Economics 151, 3–50.Google Scholar
Lane, D. A. 1993a. Artificial worlds and economics, part I. Journal of Evolutionary Economics 3, 89–107.CrossRefGoogle Scholar
Lane, D. A. 1993b. Artificial worlds and economics, part II. Journal of Evolutionary Economics 3, 177–197.CrossRefGoogle Scholar
Leijonhufvud, A. 2006. Episodes in a century of macroeconomics. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Lindley, D. 1994. The End of Physics. Basic Books.CrossRefGoogle Scholar
Malerba, F., Nelson, R., Orsenigo, L., Winter, S. 2008. Public policies and changing boundaries of firms in a history-friendly model of the co-evolution of the computer and semiconductor industries. Journal of Economic Behavior and Organization 67, 355–380.CrossRefGoogle Scholar
Malerba, F., Orsenigo, L. 2002. Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model. Industrial and Corporate Change 11, 667–703.CrossRefGoogle Scholar
Mankiw, G. N., Romer, D. (eds) 1991. New Keynesian Economics. MIT Press.Google Scholar
Mannaro, K., Marchesi, M., Setzu, A. 2008. Using an artificial financial market for assessing the impact of Tobin-like transaction taxes. Journal of Economic Behavior and Organization 67, 445–462.CrossRefGoogle Scholar
Mantel, R. 1974. On the characterization of aggregate excess demand. Journal of Economic Theory 7, 348–353.CrossRefGoogle Scholar
Mehrling, P. 2006. The problem of time in the DSGE model and the post Walrasian alternative. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Midgley, D., Marks, R., Cooper, L. 1997. Breeding competitive strategies. Management Science 43, 257–275.CrossRefGoogle Scholar
Mishkin, F. S. 2007. Will Monetary Policy Become More of a Science. Working paper 13566. NBER.CrossRefGoogle Scholar
Moss, S. 2002. Policy analysis from first principles. Proceedings of the National Academy of Sciences of the United States of America 99, 7267–7274.Google Scholar
Nelson, R., Winter, S. 1982. An Evolutionary Theory of Economic Change. The Belknap Press of Harvard University Press.Google Scholar
Neugart, M. 2008. Labor market policy evaluation with ACE. Journal of Economic Behavior and Organization 67, 418–430.CrossRefGoogle Scholar
Pyka, A., Fagiolo, G. 2007. Agent-based modelling: a methodology for neo-Schumpeterian economics. In The Elgar Companion to Neo-Schumpeterian Economics, Hanusch, H. & Pyka, A. (eds). Edward Elgar Publishers.Google Scholar
Ravenna, F. 2007. Vector autoregressions and reduced form representations of DSGE models. Journal of Monetary Economics 54, 2048–2064.CrossRefGoogle Scholar
Rotemberg, J., Woodford, M. 1999. Interest rate rules in an estimated sticky price model. In Monetary Policy Rules, Taylor, J. (ed.). University of Chicago Press.Google Scholar
Ruperez-Micola, A., Banal-Estanol, A., Bunn, D. 2008. Incentives and coordination in vertically related energy markets. Journal of Economic Behavior and Organization 67, 381–392.CrossRefGoogle Scholar
Russo, A., Catalano, M., Gallegati, M., Gaffeo, E., Napoletano, M. 2007. Industrial dynamics, fiscal policy and R&D: evidence from a computational experiment. Journal of Economic Behavior and Organization 64, 426–447.CrossRefGoogle Scholar
Schmitt-Grohé, S., Uribe, M. 2000. Price level determinacy and monetary policy under a balanced-budget requirement. Journal of Monetary Economics 45, 211–246.CrossRefGoogle Scholar
Smets, F., Wouters, R. 2003. An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1, 1123–1175.CrossRefGoogle Scholar
Sonnenschein, H. 1972. Market excess demand functions. Econometrica 40, 549–556.CrossRefGoogle Scholar
Summers, L. 1991. The scientific illusion in empirical macroeconomics. Scandinavian Journal of Economics 932, 129–148.CrossRefGoogle Scholar
Sun, J., Tesfatsion, L. 2007. Dynamic testing of wholesale power market designs: an open-source agent-based framework. Computational Economics 30, 291–327.CrossRefGoogle Scholar
Taylor, J. 1993. Discretion versus policy rules in practice. Carnegie-Rochester Series on Public Policy 39, 195–214.CrossRefGoogle Scholar
Taylor, J. 2007. The Explanatory Power of Monetary Policy Rules. Working paper 13685. NBER.CrossRefGoogle Scholar
Tesfatsion, L. 2006a. Ace: a constructive approach to economic theory. In Handbook of Computational Economics II: Agent-Based Computational Economics, Tesfatsion, L. & Judd, K. (eds). North Holland.Google Scholar
Tesfatsion, L. 2006b. Agent-based computational modeling and macroeconomics. In Post Walrasian Macroeconomics, Colander, D. (ed.). Cambridge University Press.Google Scholar
Tesfatsion, L., Judd, K. (eds) 2006. Handbook of Computational Economics II: Agent-Based Computational Economics. North Holland.Google Scholar
Wilhite, A., Allen, W. 2008. Crime, protection, and incarceration. Journal of Economic Behavior and Organization 67, 481–494.CrossRefGoogle Scholar
Woodford, M. 2003. Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press.Google Scholar
Zarnowitz, V. 1985. Recent works on business cycles in historical perspectives: a review of theories and evidence. Journal of Economic Literature 23, 523–580.Google Scholar
Zarnowitz, V. 1997. Business Cycles Observed and Assessed: Why and How They Matter. Working paper 6230. NBER.CrossRefGoogle Scholar