Talk on where macroeconomics went wrong
I gave a short talk yesterday with this title, which takes some of the main points from my paper in the OXREP ‘Rebuilding Macro’ volume It is mainly of interest to economists, or those interested in economic methodology or the history of macroeconomic thought. When I talk about macroeconomics and macroeconomists below I mean mainstream academic economists.
I want to talk today about where macroeconomics went wrong. Now it seems that this is a topic where everyone has a view. But most of those views have a common theme, and that is a dislike of DSGE models. Yet DSGE models are firmly entrenched in academic macroeconomics, and in pretty well every economist that has done a PhD, which is why the Bank of England’s core model is DSGE. To understand why DSGE is so entrenched, I need to tell the story of the New Classical Counter Revolution (NCCR).
If you had to pick a paper that epitomised the NCCR it would be “After Keynesian Macroeconomics” by Lucas and Sargent. Now from the title you would think this was an attack on Keynesian Economics, and in part it was. But we know that revolution failed. Very soon after the NCCR we had the birth of New Keynesian economics that recast key aspects of Keynesian economics within a microfoundations  framework, and is now the way nearly all central banks think about stabilisation policy. But if you read the text of Lucas and Sargent it is mainly a manifesto about how to do macroeconomics, or what I think we can reasonably call the methodology of macroeconomics.And on that their revolution was successful, and it is why nearly all academic macro is DSGE.
Before Lucas and Sargent complete macroeconomic models, of both a theoretical and empirical kind, had justified their aggregate equation using an eclectic mix of theory and econometrics. Microfoundations were used as a guide to aggregate equation specification, but if this equation fell apart in statistical terms when confronted with data in would not become part of an empirical model, and would be shunned for inclusion in theoretical models. Off course ‘falling apart ‘ is a very subjective criteria, and every effort would be made to try and make an equation consistent with microfoundations, but typically a lot of the dynamics in these models were what we would now call ad hoc, which in this case meant data based. .
Lucas famously showed that models of this kind were subject to what we call the Lucas critique , and that forms an important part of Lucas and Sargent paper. They argue that the only certain way to get round that critique is to build the model from internally consistent microfoundations. But they also ask why wouldn’t you want to build any macroeconomic model that way? Why wouldn’t you want a model where you could be sure that aggregate outcomes were the result of agents behaving in a consistent manner
If you want to crystallise why this was a methodological revolution, think about what we might call admissibility criteria for macro models. In pre-NCCR models equations were selected through an eclectic mixture of theory-fit and evidence-fit. In the RBC and later DSGE models internal theoretical consistency is an admissibility criteria. Or to put it another way, a DSGE model never got rejected because one of its equations didn’t fit the data, but if one equation had a theoretical foundation that was inconsistent with the others it would certainly not be published in the better journals.
Have a look at almost any macro paper in a top journal today, and compare it to a similar paper before the NCCR, and you can see we have been through a methodological revolution. Unfortunately many economists who have only been taught and who only known DSGE just think of this as progress. But it is not just progress, because DSGE models involve a shift away from the data. This is inevitable if you change the admissibility criteria away from the data. It inevitably means macroeconomists start focusing on models where it is easy to ensure internal theoretical consistency, and away from macroeconomic phenomenon that are clear in the data but more difficult to microfound.
If you are expecting me at this point to say that DSGE models where were macroeconomics went wrong, you will be disappointed. I spent the last 15 years of my research career building and analysing DSGE models, and I learnt a lot as a result. The mistake was the revolution part. In the US, DSGE models replaced traditional modelling within almost a decade . In my view DSGE models should have coexisted with more traditional modelling, each tolerating the other.
To get a glimpse of how that can happen look at the UK, where a traditional macromodelling scene remained active until the end of the millenium. Traditional models didn’t stand sill, but changed by adopting many of the ideas from DSGE such as rational expectations. Here the account gets a little personal, because before I did DSGE I built one of those models, called COMPACT. There are not many macroeconomists who have built and operated both traditional and DSGE models, which I think gives me some insight of the merits of both.
COMPACT was a rational expectations New Keynesian model with explicit credit constraints in a Blanchard-Yaari type consumption function, a vintage production model, and variety effects on trade. So in terms of theoretical ideas it was far richer than any DSGE model I subsequently worked with. Most of COMPACT’s behavioural equations were econometrically estimated, but it was not an internally consistent model like DSGE.
COMPACT had an explicit but exogenous credit constraint variable in the model because in our view it was impossible to model consumption behaviour over time without it. Our work was based heavily on work in the UK by John Muellbauer, and Chris Carroll was coming to similar conclusions for the US. But DSGE models never faced that issue because they worked with de-trended data. Let me spell out why that was important. Empirical work was establishing that you could not begin to understand consumption behaviour over a 20/30 year time horizon without seeing how the financial sector had changed over time, and at least one traditional macroeconomic model was incorporating that finding before the end of the last millennium.. Extensive work on exactly that issue did not begin using DSGE models until after the financial crisis, where changes in the financial sector had a critical impact on the real economy. DSGE was behind the curve, but more traditional macroeconomics was not. .
Now I don’t think it is fanciful to think that if at least some macroeconomists had continued working with more traditional data-based models alongside those doing DSGE, at least one of those models would have thought to endogenise the financial sector which was determining those varying credit constraints.
So the claim I want to make is rather a big one. If DSGE models had continued alongside more traditional, data-based modelling, economists would have been much better prepared for the financial crisis when it came. If these two methodologies had learnt from each other, DSGE models might have started focusing on the financial sector before the crisis. Of course I would never suggest that macroeconomics could have predicted that crisis, but macroeconomists would have certainly had much more useful things to say about the impact on the economy when it happened.
Just being able to imagine this being true illustrates that moving to DSGE involved losses as well as gains. It inevitably made models less rich and moved them further away from the data in areas that were difficult but not impossible to model in a theoretically consistent way. The DSGE methodological revolution set out so clearly in Lucas and Sargent’s paper changed the focus of macroeconomics away from things we now know were of critical importance.
I’ve been talking about this since I started writing a blog at the end of 2011, but recently we have seen similar messages from Paul Romer and Olivier Blanchard in this OxREP volume. What I have called here traditional models, and in the paper I call Structural Econometric Models, Blanchard calls provocatively policy models. It was provocative because most academic macroeconomists think DSGE models are the only models that can do policy analysis ‘properly’, but Blanchard suggests policymakers want models that are closer to the data more than they want a guarantee of internal consistency, and they want models that are quick and easy to adapt to unfolding problems. The US Fed, although it has a DSGE model, also has a more traditional model that has similarities to a traditional model like COMPACT, and guess which model plays the major role in the policy process?
 Microfoundations means deriving aggregate equations from microeconomic optimisation behaviour
 The Lucas critique argued that many equations of traditional macroeconomic models embodied beliefs about macro policy, and so if policy changed the equations would no longer be valid.
 The difficulty of identification in single equation estimation highlighted by Sims in 1980 probably also contributed. .