During my recent hiatus from blogging, I have been pondering an important paper presented in June at the History of Economics Society meeting in Toronto, “The Standard Narrative on History of Macroeconomics: Central Banks and DSGE Models” by Francesco Sergi of the University of Bristol, which was selected by the History of Economics Society as the best conference paper by a young scholar in 2017.
Here is the abstract of Sergi’s paper:
How do macroeconomists write the history of their own discipline? This article provides a careful reconstruction of the history of macroeconomics told by the practitioners working today in the dynamic stochastic general equilibrium (DSGE) approach.
Such a tale is a “standard narrative”: a widespread and “standardizing” view of macroeconomics as a field evolving toward “scientific progress”. The standard narrative explains scientific progress as resulting from two factors: “consensus” about theory and “technical change” in econometric tools and computational power. This interpretation is a distinctive feature of central banks’ technical reports about their DSGE models.
Furthermore, such a view on “consensus” and “technical change” is a significantly different view with respect to similar tales told by macroeconomists in the past — which rather emphasized the role of “scientific revolutions” and struggles among competing “schools of thought”. Thus, this difference raises some new questions for historians of macroeconomics.
Sergi’s paper is too long and too rich in content to easily summarize in this post, so what I will do is reproduce and comment on some of the many quotations provided by Sergi, taken mostly from central-bank reports, but also from some leading macroeconomic textbooks and historical survey papers, about the “progress” of modern macroeconomics, and especially about the critical role played by “microfoundations” in achieving that progress. The general tenor of the standard narrative is captured well by the following quotations from V. V. Chari
[A]ny interesting model must be a dynamic stochastic general equilibrium model. From this perspective, there is no other game in town. […] A useful aphorism in macroeconomics is: “If you have an interesting and coherent story to tell, you can tell it in a DSGE model. (Chari 2010, 2)
I could elaborate on this quotation at length, but I will just leave it out there for readers to ponder with a link to an earlier post of mine about methodological arrogance. Instead I will focus on two other sections of Sergi’s paper “the five steps of theoretical progress” and “microfoundations as theoretical progress.” Here is how Sergi explains the role of the five steps:
The standard narrative provides a detailed account of the progressive evolution toward the synthesis. Following a teleological perspective, each step of this evolution is an incremental, linear improvement of the theoretical tool box for model building. The standard narrative identifies five steps . . . . Each step corresponds to the emergence of a school of thought. Therefore, in the standard narrative, there are not such things as competing schools of thought and revolutions. Firstly, because schools of thought are represented as a sequence; one school (one step) is always leading to another school (the following step), hence different schools are not coexisting for a long period of time. Secondly, there are no revolutions because, while emerging, new schools of thought [do] not overthrow the previous ones; instead, they suggest improvements and amendments, that are accepted as an improvement by pre-existing schools therefore, accumulation of knowledge takes place thanks to consensus. (pp. 17-18)
The first step in the standard narrative is the family of Keynesian macroeconometric models of the 1950s and 1960s, the primitive ancestors of the modern DSGE models. The second step was the emergence of New Classical macroeconomics which introduced the ideas of rational expectations and dynamic optimization into theoretical macroeconomic discourse in the 1970s. The third step was the development, inspired by New Classical ideas, of Real-Business-Cycle models of the 1980s, and the fourth step was introduction of New Keynesian models in the late 1980s and 1990s that tweaked the Real-Business-Cycle models in ways that rationalized the use of counter-cyclical macroeconomic policy within the theoretical framework of the Real-Business-Cycle approach. The final step, the DSGE model, emerged more or less naturally as a synthesis of the converging Real-Business-Cycle and New Keynesian approaches.
After detailing the five steps of theoretical progress, Sergi focuses attention on “the crucial improvement” that allowed the tool box of macroeconomic modelling to be extended in such a theoretically fruitful way: the insistence on providing explicit microfoundations for macroeconomic models. He writes:
Abiding [by] the Lucasian microfoundational program is put forward by DSGE modellers as the very fundamental essence of theoretical progress allowed by [the] consensus. As Sanajay K. Chugh (University of Pennsylvania) explains in the historical chapter of his textbook, microfoundations is all what modern macroeconomics is about: (p. 20)
Modern macroeconomics begin by explicitly studying the microeconomic principles of utility maximization, profit maximization and market-clearing. [. . . ] This modern macroeconomics quickly captured the attention of the profession through the 1980s [because] it actually begins with microeconomic principles, which was a rather attractive idea. Rather than building a framework of economy-wide events from the top down [. . .] one could build this framework using microeconomic discipline from the bottom up. (Chugh 2015, 170)
Chugh’s rationale for microfoundations is a naïve expression of reductionist bias dressed up as simple homespun common-sense. Everyone knows that you should build from the bottom up, not from the top down, right? But things are not always quite as simple as they seem. Here is an attempt to present microfoundations as being cutting-edge and sophisticated offered in a 2009 technical report written by Cuche-Curti et al. for the Swiss National Bank.
The key property of DSGE models is that they rely on explicit micro-foundations and a rational treatment of expectations in a general equilibrium context. They thus provide a coherent and compelling theoretical framework for macroeconomic analysis. (Cuche-Curti et al. 2009, 6)
A similar statement is made by Gomes et al in a 2010 technical report for the European Central Bank:
The microfoundations of the model together with its rich structure allow [us] to conduct a quantitative analysis in a theoretically coherent and fully consistent model setup, clearly spelling out all the policy implications. (Gomes et al. 2010, 5)
These laudatory descriptions of the DSGE model stress its “coherence” as a primary virtue. What is meant by “coherence” is spelled out more explicitly in a 2006 technical report describing NEMO, a macromodel of the Norwegian economy, by Brubakk et al. for the Norges Bank.
Various agents’ behavior is modelled explicitly in NEMO, based on microeconomic theory. A consistent theoretical framework makes it easier to interpret relationships and mechanisms in the model in the light of economic theory. One advantage is that we can analyse the economic effects of changes of a more structural nature […] [making it] possible to provide a consistent and detailed economic rationale for Norges Bank’s projections for the Norwegian economy. This distinguishes NEMO from purely statistical models, which to a limited extent provide scope for economic interpretations. (Brubakk and Sveen 2009, 39)
By creating microfounded models, in which all agents are optimizers making choices consistent with the postulates of microeconomic theory, DSGE model-builders, in effect, create “laboratories” from which to predict the consequences of alternative monetary policies, enabling policy makers to make informed policy choices. I pause merely to note and draw attention to the tendentious and misleading misappropriation of the language of empirical science by these characteristically self-aggrandizing references to DSGE models as “laboratories” as if what was going on in such models was determined by an actual physical process, as is routinely the case in the laboratories of physical and natural scientists, rather than speculative exercises in high-level calculations derived from the manipulation of DSGE models.
As a result of recent advances in macroeconomic theory and computational techniques, it has become feasible to construct richly structured dynamic stochastic general equilibrium models and use them as laboratories for the study of business cycles and for the formulation and analysis of monetary policy. (Cuche-Curri et al. 2009, 39)
Policy makers can be confident that the conditional predictions corresponding to the policy alternative under consideration, which are derived from their “laboratory” DSGE models, because those models, having been constructed on the basis of the postulates of economic theory, are therefore microfounded, embodying deep structural parameters that are invariant to policy changes. Microfounded models are thus immune to the Lucas Critique of macroeconomic policy evaluation, under which the empirically estimated coefficients of traditional Keynesian macroeconometric models cannot be assumed to remain constant under policy changes, because those coefficient estimates are themselves conditional to policy choices.
Here is how the point is made in three different central bank technical reports: by Argov et al. in a 2012 technical report about MOISE, a DSGE model for the Israeli economy, by Cuche-Curti et al. and by Medina and Soto in a 2006 technical report about a new DSGE model for the Chilean economy for the Central Bank of Chile.
Being micro-founded, the model enables the central bank to assess the effect of its alternative policy choices on the future paths of the economy’s endogenous variables, in a way that is immune to the Lucas critique. (Argov et al. 2012, 5)
[The DSGE] approach has three distinct advantages in comparison to other modelling strategies. First and foremost, its microfoundations should allow it to escape the Lucas critique. (Cuche-Curti et al. 2009, 6)
The main advantage of this type of model, over more traditional reduce-form macro models, is that the structural interpretation of their parameters allows [it] to overcome the Lucas Critique. This is clearly an advantage for policy analysis. (Medina and Soto, 2006, 2)
These quotations show clearly that escaping, immunizing, or overcoming the Lucas Critique is viewed by DSGE modelers as the holy grail of macroeconomic model building and macroeconomic policy analysis. If the Lucas Critique cannot be neutralized, the coefficient estimates derived from reduced-form macroeconometric models cannot be treated as invariant to policy and therefore cannot provide a secure basis for predicting the effects of alternative policies. But DSGE models allow deep structural relationships, reflecting the axioms underlying microeconomic theory, to be estimated. Because they reflect the deep, and presumably stable, microeconomic structure of the economy, estimates of deep parameters derived from DSGE models, DSGE modelers claim that these estimates provide policy makers with a reliable basis for conditional forecasting of the effects of macroeconomic policy.
Because of the consistently poor track record of DSGE models in actual forecasting (for evidence of that poor track record see the paper by Carlaw and Lipsey and my post about their paper) comparing the predictive performance of DSGE models with more traditional macroeconometric models), the emphasis placed on the Lucas Critique by DSGE modelers has an apologetic character, DSGE modelers having to account for the relatively poor comparative predictive power of DSGE models by relentlessly invoking the Lucas Critique in trying to account for, and explain away, the poor predictive performance of the DSGE models. But if DSGE models really are better than traditional macro models why are their unconditional predictions not at least as good as those of traditional macroeconometric models? Obviously estimates of the deep structural relationships provided by microfounded models are not as reliable as DSGE apologetics tries to suggest.
And the reason that the estimates of deep structural relationships derived from DSGE models are not reliable is that those models, no less than traditional macroeconometric models, are subject to the Lucas Critique, the deep microeconomic structural relationships embodied in DSGE models being conditional on the existence of a unique equilibrium solution that persists long enough for the structural relationships characterizing that equilibrium to be inferred from the data-generating mechanism whereby those models are estimated. (I have made this point previously here.) But if the data-generating mechanism does not conform to the unique general equilibrium upon whose existence the presumed deep structural relationships of microeconomic theory embodied in DSGE models are conditioned, the econometric estimates derived from DSGE models cannot capture the desired deep structural relationships, and the resulting structural estimates are therefore incapable of providing a reliable basis for macroeconomic-policy analysis or for conditional forecasts of the effects of alternative policies, much less unconditional forecasts of endogenous macroeconomic variables.
Of course, the problem is even more intractable than the discussion above implies, because there is no reason why the deep structural relationships corresponding to a particular equilibrium should be invariant to changes in the equilibrium. So any change in economic policy that displaces a pre-existing equilibrium, let alone any other unforeseen technological change or change in tastes or resource endowments that displaces a pre-existing equilibrium will necessarily cause all the deep structural relationships to change correspondingly. So the deep structural parameters upon whose invariance the supposedly unique capacity of DSGE models to provide policy analysis upon which policy makers can rely simply don’t exist. Policy making based on DSGE models is as much an uncertain art requiring the exercise of finely developed judgment and intuition as policy making based on any other kind of economic modeling. DSGE models provide no uniquely reliable basis for making macroeconomic policy.
References
Uneasy Money 