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

For an introduction, see Woodford (2003) and Galí and Gertler (2007). Cf. also Colander (2006b) for a historical perspective.

This stance strongly contrasts with that of many policymakers. For example, Alan Greenspan argued that ‘despite extensive effort to capture and quantify what we perceive as the key macroeconomic relationships, our knowledge about many of the important linkages is far from being complete and, in all likelihood, will always remain so’ (Greenspan, 2004: 37). An extremely pessimistic view on the possibility of taking any economic model seriously econometrically is in Summers (1991). On these points see also Mehrling (2006).

For a thorough discussion of the limits of the DGSE synthesis we refer the reader to Colander (2006c) and articles therein.

For example, neoclassical economics is still far from developing a common model where different policy issues related to economic growth may be evaluated (an alternative view is discussed in Aghion and Howitt, 2007). For these reasons we chose not to consider long-run macro-economic issues here.

This term was first introduced by Goodfriend and King (1997). Woodford (2003) labeled the approach as ‘Neo Wicksellian’. As stated by Galí and Gertler (2007) the term ‘New Keynesian’ is the most used, even if earlier New Keynesian models were very different from the ones of the New Neoclassical Synthesis.

For a formal derivation of the NNS model see Goodfriend and King (1997), Clarida et al. (1999), Woodford (2003), and Galí (2008).

Of course, also other monetary policy rules different from the Taylor rule (cf. Equation (3)) can lead to a local determinate rational-expectation equilibrium.

See also Beyer and Farmer (2004).

Fukac and Pagan (2006) also argue that identification problems are usually partly mitigated by arbitrarily assumed serially correlated shocks.

On the contrary, the London School of Economics (LSE)-Copenhagen school follows a macroeconometric modeling philosophy orthogonal to the one followed by DSGE modelers. Indeed, scholars of the LSE-Copenhagen approach do not impose any theory-driven restrictions to macroeconometric models, but they ‘allow the data to speak freely’ (Hoover et al., 2008), that is, they concentrate their efforts on improving the statistical model in order to structure data with an identified cointegrated vector autoregression that could then be used to produce stylized facts for theoretical models (Johansen & Juselius, 2006; Juselius & Franchi, 2007). In this respect, the LSE-Copenaghen school can be considered complementary to agent-based computational economics in that it can produce an ensemble of macroeconomic stylized facts that ACE models should try to succesfully account for.

Citing a very provocative sentence by Giovanni Dosi, this way of theorizing is like claiming that biology stems from thermodynamics equilibrium with some imperfections.

The philosophical underpinnings of ACE largely overlap with those of similar, complementary, approaches known in the literature as ‘Post Walrasian Macroeconomics’ (Colander, 2006c) and ‘Evolutionary Economics’ (Nelson & Winter, 1982; Dosi & Nelson, 1994). The overlap is often so strong that one might safely speak of an emerging ‘heterodox synthesis’. Historically, the first attempt to develop agent-based economics can be traced back to Marshall (Leijonhufvud, 2006).

This and the following subsections heavily draw from Pyka and Fagiolo (2007) and Fagiolo et al. (2007). For further details see, among others, Dosi and Egidi (1991), Lane and Maxfield (2004), Dosi et al. (2005), Tesfatsion and Judd (2006), Colander (2006a) and Tesfatsion (2006b).

This has to be contrasted with the neoclassical approach, where agents hold rational expectations and, as Mehrling (2006: 76) puts it, ‘the future, or rather our ideas about the future, determines the present’.

For a more in-depth discussion of empirical validation in ABMs, we refer the reader to Fagiolo et al. (2007) and papers therein.

Note that some of these arguments also apply, mutatis mutandum, to validation of DSGE models. However, selection between alternative DSGE formulations is done, if at all, on the basis of their analytical tractability and elegance, and almost never on their empirical validity.

See for example the papers contained in the special issue ‘Agent-Based Models for Economic Policy Design’ edited by Dawid and Fagiolo (2008).

Moss (2002) discusses the importance of involving the actual decision makers in the process of the generation of agent-based models for policy evaluation.

Ruperez-Micola et al. (2008) consider a stylized model of the value chain in electricity markets and study the problem of the emergence of vertical market power.

Happe et al. (2008) assess the effect of a regime switch in the way agricultural subsidies are paid on changes in farm structure, prices, and farm profits.

Chen and Chie (2008) address the classical question of determining the tax-revenue-maximizing tax rate in the framework of lottery markets and explain the puzzle why lottery tax rates vary substantially between different countries and lotteries.

In a nutshell, the model describes an economy composed of firms and consumers/workers. Firms belong to two industries. Firms in the first industry perform R&D and produce heterogeneous machine tools. Firms in the second industry invest in new machines and produce a homogenous consumption good. Consumers sell their labor and consume their income.

Dawid et al. (2008) study the impact of policies enhancing workers’ skills on economic growth and the performance of the labor market.