The recent passing of Ed Prescott, the 2004 Nobel Laureate in economics, was a cause for sadness across the economics profession. Prescott was universally recognised as a revolutionary thinker in the field of macroeconomics and one of his great innovations (along with fellow Laureate Finn Kydland) was the introduction of so-called Real Business Cycle (RBC) models. In simple terms, these models postulate that business cycle fluctuations arise as a result of labour supply decisions in response to stochastic shocks. One of the consequences of this paradigm is that business cycles are optimal responses to productivity shocks and that interventions to offset such shocks are harmful because they cause the economy to deviate from its long-run optimal path.
The first attempt to produce an economic model based on these principles was Prescott’s 1986 paper ‘Theory Ahead of Business Cycle Measurement’ which was very much based on calibrated responses rather than one which used statistical techniques to fit the data. It was also a model which assumed a world in which there were no distortions. Unsurprisingly, Keynesian economists did not take the RBC conclusions lying down. They argued that the economy was characterised by frictions such as nominal rigidities, the existence of monopoly power and information asymmetries which can result in involuntary unemployment, thus opening up a role for governments to smooth the cycle. In response, the so-called New Keynesians devised a model paradigm which required the imposition of a number of restrictive assumptions in order to approximate the world as they saw it.
Thus did the literature on New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models come into being: Dynamic because they operate over very long (infinite) horizons; Stochastic because they deal with random shocks and General Equilibrium because they are built up from microfoundations. Such models now dominate much of the academic thinking in the modelling and policy literature. But they are mathematically complex, opaque and founded on a series of assumptions that calls into question whether they have anything useful to contribute to the future of macroeconomics[1].
What’s not to like? Quite a lot as it happens!
In the words of Olivier Blanchard, “there are many reasons to dislike current DSGE models” particularly because of the apparently arbitrary nature of the assumptions on which they are based. For example, aggregate demand is based on infinitely lived households which are assumed to have perfect foresight. Show me one of those and I will give you some hen’s teeth. Furthermore, the inflation equation is based on a forward looking equation that does not take any account of inflation persistence. But perhaps the most contestable features of DGSE models is their slavish adherence to microfoundations. These attempt to embed economic behaviour patterns that are invariant to a particular state of the world. This allows macroeconomics to escape from the charge posed by the Lucas critique that the parameters of any model change as circumstances change – a criticism of the models in operation in the 1970s, and which was perceived to be one of the reasons why they performed so badly in predicting the recessions of the time.
There is a lot wrong with this way of thinking. For one thing, the microfoundations are based on the behaviour of representative agents. In other words, they impose a theory of how individual firms and households act and assume that we can scale this up to the wider economy. As one who grew up using models based on aggregate data, it has always struck me as odd that we should discard much of the richness inherent in the observational evidence of macro data. An interesting theoretical paper published in 2020 makes the more subtle point that for the representative agent to mimic the preference structure of the population requires the imposition of extreme restrictions on the utility function used to describe household behaviour. The supreme irony of this is that the DSGE revolution was able to capture the intellectual high ground because the structural modelling paradigm that it replaced was unable to counter the criticism levelled in Chris Sims’s classic 1980 paper that such models relied on “incredible” identifying assumptions.
A further thought is that we have little evidence that the utility functions of representative agents are invariant over time, as modern macro theory assumes. But whilst there is no doubt that the research underpinning the macro revolution in the late-1970s and early-1980s – including the influential work of Lucas – is intellectually persuasive, the evidence of this year alone, in which inflation spiked to 40-year highs unforeseen by most models in 2021, does not persuade me that the DSGE revolution has significantly enhanced the thinking in modern macro.
By this point you have probably gathered that I am highly sceptical of much of the work conducted in modern macro modelling in the last 40 or so years. This is not to deny that it is intellectually fascinating and I am more than happy to play around with DSGE models. But as Anton Korinek points out in this fascinating essay, “DSGE models aim to quantitatively describe the macroeconomy in an engineering-like fashion.” They fall victim to the “mathiness” in economics, of which Paul Romer was so scathing.
And their forecasting performance is poor
We might be more accepting of the DSGE paradigm if it produced significantly better forecasting results than what went before. The events of the past 15 years suggest that this is far from the case and it is now generally acknowledged that the out-of-sample forecasting performance of DSGE models is very poor. If this does not render them useless as a forecasting tool, it suggests that they are no better than the structural models which the academic community has spent forty years trying to knock down. Proponents will argue that this is not what they are designed to do. Rather they are designed to understand how the economy is constructed around the deep-seated parameters underpinning household and corporate decision-making which allows for policy evaluation.
This debate was brought into sharp focus recently following the publication of a fascinating paper on the properties of DSGE models which is less concerned about whether they represent good economics but whether they represent good models in a statistical sense. The answer, according to the authors, is that they do not. The paper can get quite dense in places but one of the things it does is to examine how well it can fit nonsense data. By randomly swapping the series around and feeding them into the DSGE model, “much of the time we get a model which predicts the [nonsense] data better than the model predicts the [actual] data.” They draw the damning conclusion that “even if one disdains forecasting as an end in itself, it is hard to see how this is at all compatible with a model capturing something – anything – essential about the structure of the economy.”
Last word
As one who for many years has used models for forecasting purposes that have been sniffily dismissed by the academic community, it is hard to avoid a sense of schadenfreude. Blanchard offers us a way out of this impasse, arguing that theoretical models of the economy have a role to play in “clarifying theoretical issues within a general equilibrium setting ... In short, [they] should facilitate the debate among macro theorists.” By contrast, policy models of the type with which I am most comfortable, “should fit the main characteristics of the data” and be used for forecasting and policy analysis.
There is some merit in this argument. By all means continue to tinker with DSGE models to see what kinds of insight they can generate but do not let them anywhere near the real world until their forecast performance substantially improves. In the words of statistician George Box, “all models are wrong but some are useful”. And some are DSGE models.
[1] For an excellent introduction to many of the issues in modern macro, check out this free online textbook ‘Advanced Macroeconomics: An Easy Guide’ By Filipe Campante, Federico Sturzenegger and Andrés Velasco
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