The day macroeconomics changed

It is of course ludicrous, but who cares. The day of the Boston Fed conference in 1978 is fast taking on a symbolic significance. It is the day that Lucas and Sargent changed how macroeconomics was done. Or, if you are Paul Romer, it is the day that the old guard spurned the ideas of the newcomers, and ensured we had a New Classical revolution in macro rather than a New Classical evolution. Or if you are Ray Fair (HT Mark Thoma), who was at the conference, it is the day that macroeconomics started to go wrong.

Ray Fair is a bit of a hero of mine. When I left the National Institute to become a formal academic, I had the goal (with the essential help of two excellent and courageous colleagues) of constructing a new econometric model of the UK economy, which would incorporate the latest theory: in essence, it would be New Keynesian, but with additional features like allowing variable credit conditions to influence consumption. Unlike a DSGE it would as far as possible involve econometric estimation. I had previously worked with the Treasury’s model, and then set up what is now NIGEM at the National Institute by adapting a global model used by the Treasury, and finally I had been in charge of developing the Institute’s domestic model. But creating a new model from scratch within two years was something else, and although the academics on the ESRC board gave me the money to do it, I could sense that some of them thought it could not be done. In believing (correctly) that it could, Ray Fair was one of the people who inspired me.

I agree with Ray Fair that what he calls Cowles Commission (CC) type models, and I call Structural Econometric Model (SEM) type models, together with the single equation econometric estimation that lies behind them, still have a lot to offer, and that academic macro should not have turned its back on them. Having spent the last fifteen years working with DSGE models, I am more positive about their role than Fair is. Unlike Fair, I want “more bells and whistles on DSGE models”. I also disagree about rational expectations: the UK model I built had rational expectations in all the key relationships.

Three years ago, when Andy Haldane suggested that DSGE models were partly to blame for the financial crisis, I wrote a post that was critical of Haldane. What I thought then, and continue to believe, is that the Bank had the information and resources to know what was happening to bank leverage, and it should not be using DSGE models as an excuse for not being more public about their concerns at the time.

However, if we broaden this out from the Bank to the wider academic community, I think he has a legitimate point. I have talked before about the work that Carroll and Muellbauer have done which shows that you have to think about credit conditions if you want to explain the pre-crisis time series for UK or US consumption. DSGE models could avoid this problem, but more traditional structural econometric (aka CC) models would find it harder to do so. So perhaps if academic macro had given greater priority to explaining these time series, it would have been better prepared for understanding the impact of the financial crisis.

What about the claim that only internally consistent DSGE models can give reliable policy advice? For another project, I have been rereading an AEJ Macro paper written in 2008 by Chari et al, where they argue that New Keynesian models are not yet useful for policy analysis because they are not properly microfounded. They write “One tradition, which we prefer, is to keep the model very simple, keep the number of parameters small and well-motivated by micro facts, and put up with the reality that such a model neither can nor should fit most aspects of the data. Such a model can still be very useful in clarifying how to think about policy.” That is where you end up if you take a purist view about internal consistency, the Lucas critique and all that. It in essence amounts to the following approach: if I cannot understand something, it is best to assume it does not exist.

Judgement Calls and Microfoundation Tricks

Noah Smith has a really nice piece about when a microfounded model does or does not violate the Lucas critique. (See also this useful post from Bruegel.) Noah suggests that this comes down to a judgement call, which in turn introduced a potential ideological bias. I want to elaborate, and suggest another bias that may result: a bias towards simplicity. However I also want to suggest a further bias that potentially undercuts the methodological rationale behind microfoundations.

The key idea behind the Lucas critique was that models should be derived from ’deep’ parameters, like agents preferences or technological parameters. These were parameters that could reasonably be described as independent of the way monetary policy was conducted. The target of the Lucas critique was models where expectations formation was implicit in the model’s equations: even if you only half believed in rational expectations, changes in how monetary policy was done would change how expectations were formed, and therefore change those equations.

Noah argues that whether a parameter is independent of policy is essentially a judgement – our evidence base is not good enough to show us one way or another. Where you have judgement, various biases, including ideological views, can get in. I think this is right, but I also suspect the point will not bother most macroeconomists too much. They are – rightly or wrongly – fairly happy with treating preference parameters as exogenous, whereas treating expectations processes as independent of policy seems clearly problematic. (I appeal here to what most macroeconomists will think, and not what is right. In a recent post, for example, I argue that people’s preferences over which party to vote for are pretty malleable.)

However, once you go beyond the very simple RBC type models, the range of deep parameters extends beyond preferences and technology. To take the obvious example, if you want to have something useful to say about monetary policy, you need sticky prices, and these are usually microfounded in terms of Calvo contracts. The deep parameter in Calvo contracts is the probability that a firm’s price will change each period. Is this parameter independent of monetary policy?

The paper by Chari et al to which Noah refers puts the same point in a slightly different way. If the parameters of the model are not deep (independent), then the implied shocks to the model will not be ‘structural’ i.e. identifiable and independent of policy. They look at the shock processes typically included in New Keynesian models, and split them into two groups: potentially structural shocks, which include technology shocks, and dubiously structural shocks, which include mark-up shocks.

How do Chari et al decide which of these two categories shocks should be classified in? Noah would say judgement, whereas the authors would say microeconomic evidence. However this is not a debate I want to get into, interesting though it is. Instead I want to agree with Chari et al: the shocks in New Keynesian models are pretty dubious, and their deep parameters, like the Calvo parameter, are not obviously invariant to policy.

So why do New Keynesian models contain problematic features like Calvo contracts? Calvo contracts are a ‘trick’, by which I mean a device that allows you to get sticky prices into a model in a reasonably tractable way. Doing this job ‘properly’ might involve adding menu costs into the model, but this quickly gets intractable. So Calvo contracts are a trick that acts ‘as if’ firms were faced by menu costs. But whether this trick works – whether Calvo contracts really do mimic what an otherwise intractable model with menu costs would show – is inevitably a judgement call.

Because these judgement calls are problematic, there is a bias towards avoiding them by keeping the model simple. Here Chari et al are explicit. “One tradition, which we prefer, is to keep the model very simple, keep the number of parameters small and well-motivated by micro facts, and put up with the reality that such a model neither can nor should fit most aspects of the data. Such a model can still be very useful in clarifying how to think about policy.”

Suppose we do not follow this tradition, and instead attempt to explain more aspects of the data by building models that incorporate dubious judgement calls. I think we then have to recognise that these judgement calls will be influenced not just by the microeconomic evidence, or ideology as Noah suggests, but also on the need to have models that explain the real world. That is I believe a quite reasonable thing to do, but as Chari et al point out it does mean potentially compromising the internally consistency of the model (and therefore its immunity from the Lucas critique). As I have argued at length elsewhere (article, working paper), microfounded models have become dominant because they have let the evidence influence model structure through a back door. Individual equations may no longer be selected by directly confronting the data, but the data has influenced the judgemental calls involved in the microfoundations.