Ricardian Equivalence, benchmark models, and academics response to the financial crisis

Mainly for economists

In his further thoughts on DSGE models (or perhaps his response to those who took up his first thoughts), Olivier Blanchard says the following:

“For conditional forecasting, i.e. to look for example at the effects of changes in policy, more structural models are needed, but they must fit the data closely and do not need to be religious about micro foundations.”

He suggests that there is wide agreement about the above. I certainly agree, but I’m not sure most academic macroeconomists do. I think they might say that policy analysis done by academics should involve microfounded models. Microfounded models are, by definition, religious about microfoundations and do not fit the data closely. Academics are taught in grad school that all other models are flawed because of the Lucas critique, an argument which assumes that your microfounded model is correctly specified.

It is not only academics who think policy has to be done using microfounded models. The core model used by the Bank of England is a microfounded DSGE model. So even in this policy making institution, their core model does not conform to Blanchard’s prescription. (Yes, I know they have lots of other models, but still. The Fed is closer to Blanchard than the Bank.)

Let me be more specific. The core macromodel that many academics would write down involves two key behavioural relationships: a Phillips curve and an IS curve. The IS curve is purely forward looking: consumption depends on expected future consumption. It is derived from an infinitely lived representative consumer, which means Ricardian Equivalence holds in this model. As a result, in this benchmark model Ricardian Equivalence also holds. [1]

Ricardian Equivalence means that a bond financed tax cut (which will be followed by tax increases) has no impact on consumption or output. One stylised empirical fact that has been confirmed by study after study is that consumers do spend quite a large proportion of any tax cut. That they should do so is not some deep mystery, but may be traced back to the assumption that the intertemporal consumer is never credit constrained. In that particular sense academics’ core model does not fit Blanchard’s prescription that it should ‘“fit the data closely”.

Does this core model influence the way some academics think about policy? I have written how mainstream macroeconomics neglected before the financial crisis the importance that shifting credit conditions had on consumption, and speculated that this neglect owed something to the insistence on microfoundations. That links the methodology macroeconomists use, or more accurately their belief that other methodologies are unworthy, to policy failures (or at least inadequacy) associated with that crisis and its aftermath.

I wonder if the benchmark model also contributed to a resistance among many (not a majority, but a significant minority) to using fiscal stimulus when interest rates hit their lower bound. In the benchmark model increases in public spending still raise output, but some economists do worry about wasteful expenditures. For these economists tax cuts, particularly if aimed at those who are non-Ricardian, should be an attractive alternative means of stimulus, but if your benchmark model says they will have no effect, I wonder whether this (consciously or unconsciously) biases you against such measures.

In my view, the benchmark models that academic macroeconomists carry round in their head should be exactly the kind Blanchard describes: aggregate equations which are consistent with the data, and which may or may not be consistent with current microfoundations. They are the ‘useful models’ that Blanchard talked about in his graduate textbook with Stan Fischer, although then they were confined to chapter 10! These core models should be under constant challenge from both partial equilibrium analysis, estimation in all its forms and analysis using microfoundations. But when push comes to shove, policy analysis should be done with models that are the best we have at meeting all those challenges, and not models with consistent microfoundations.

[1] Recognising this point, some might add some ‘rule of thumb’ consumers into the model. This is fine, as long as you do not continue to think the model is microfounded. If these rule of thumb consumers spend all their income because of credit constraints, what happens when these constraints are expected to last for more than the next period? Does the model correctly predict what would happen to consumption if the proportion of rule of thumb consumers changes? It does not.  

Reform and revolution in macroeconomics

Mainly for economists

Paul Romer has a few recent posts (start here, most recent here) where he tries to examine why the saltwater/freshwater divide in macroeconomics happened. A theme is that this cannot all be put down to New Classical economists wanting a revolution, and that a defensive/dismissive attitude from the traditional Keynesian status quo also had a lot to do with it.

I will leave others to discuss what Solow said or intended (see for example Robert Waldmann). However I have no doubt that many among the then Keynesian status quo did react in a defensive and dismissive way. They were, after all, on incredibly weak ground. That ground was not large econometric macromodels, but one single equation: the traditional Phillips curve. This had inflation at time t depending on expectations of inflation at time t, and the deviation of unemployment/output from its natural rate. Add rational expectations to that and you show that deviations from the natural rate are random, and Keynesian economics becomes irrelevant. As a result, too many Keynesian macroeconomists saw rational expectations (and therefore all things New Classical) as an existential threat, and reacted to that threat by attempting to rubbish rational expectations, rather than questioning the traditional Phillips curve. As a result, the status quo lost. [1]

We now know this defeat was temporary, because New Keynesians came along with their version of the Phillips curve and we got a new ‘synthesis’. But that took time, and you can describe what happened in the time in between in two ways. You could say that the New Classicals always had the goal of overthrowing (rather than improving) Keynesian economics, thought that they had succeeded, and simply ignored New Keynesian economics as a result. Or you could say that the initially unyielding reaction of traditional Keynesians created an adversarial way of doing things whose persistence Paul both deplores and is trying to explain. (I have no particular expertise on which story is nearer the truth. I went with the first in this post, but I’m happy to be persuaded by Paul and others that I was wrong.) In either case the idea is that if there had been more reform rather than revolution, things might have gone better for macroeconomics.

The point I want to discuss here is not about Keynesian economics, but about even more fundamental things: how evidence is treated in macroeconomics. You can think of the New Classical counter revolution as having two strands. The first involves Keynesian economics, and is the one everyone likes to talk about. But the second was perhaps even more important, at least to how academic macroeconomics is done. This was the microfoundations revolution, that brought us first RBC models and then DSGE models. As Paul writes:

“Lucas and Sargent were right in 1978 when they said that there was something wrong, fatally wrong, with large macro simulation models. Academic work on these models collapsed.”

The question I want to raise is whether for this strand as well, reform rather than revolution might have been better for macroeconomics.

First two points on the quote above from Paul. Of course not many academics worked directly on large macro simulation models at the time, but what a large number did do was either time series econometric work on individual equations that could be fed into these models, or analyse small aggregate models whose equations were not microfounded, but instead justified by an eclectic mix of theory and empirics. That work within academia did largely come to a halt, and was replaced by microfounded modelling.

Second, Lucas and Sargent’s critique was fatal in the sense of what academics subsequently did (and how they regarded these econometric simulation models), although they got a lot of help from Sims (1980). But it was not fatal in a more general sense. As Brad DeLong points out, these econometric simulation models survived both in the private and public sectors (in the US Fed, for example, or the UK OBR). In the UK they survived within the academic sector until the latter 1990s when academics helped kill them off.

I am not suggesting for one minute that these models are an adequate substitute for DSGE modelling. There is no doubt in my mind that DSGE modelling is a good way of doing macro theory, and I have learnt a lot from doing it myself. It is also obvious that there was a lot wrong with large econometric models in the 1970s. My question is whether it was right for academics to reject them completely, and much more importantly avoid the econometric work that academics once did that fed into them.

It is hard to get academic macroeconomists trained since the 1980s to address this question, because they have been taught that these models and techniques are fatally flawed because of the Lucas critique and identification problems. But DSGE models as a guide for policy are also fatally flawed because they are too simple. The unique property that DSGE models have is internal consistency. Take a DSGE model, and alter a few equations so that they fit the data much better, and you have what could be called a structural econometric model. It is internally inconsistent, but because it fits the data better it may be a better guide for policy.

What happened in the UK in the 1980s and 1990s is that structural econometric models evolved to minimise Lucas critique problems by incorporating rational expectations (and other New Classical ideas as well), and time series econometrics improved to deal with identification issues. If you like, you can say that structural econometric models became more like DSGE models, but where internal consistency was sacrificed when it proved clearly incompatible with the data.

These points are very difficult to get across to those brought up to believe that structural econometric models of the old fashioned kind are obsolete, and fatally flawed in a more fundamental sense. You will often be told that to forecast you can either use a DSGE model or some kind of (virtually) atheoretical VAR, or that policymakers have no alternative when doing policy analysis than to use a DSGE model. Both statements are simply wrong.

There is a deep irony here. At a time when academics doing other kinds of economics have done less theory and become more empirical, macroeconomics has gone in the opposite direction, adopting wholesale a methodology that prioritised the internal theoretical consistency of models above their ability to track the data. An alternative - where DSGE modelling informed and was informed by more traditional ways of doing macroeconomics - was possible, but the New Classical and microfoundations revolution cast that possibility aside.

Did this matter? Were there costs to this strand of the New Classical revolution?

Here is one answer. While it is nonsense to suggest that DSGE models cannot incorporate the financial sector or a financial crisis, academics tend to avoid addressing why some of the multitude of work now going on did not occur before the financial crisis. It is sometimes suggested that before the crisis there was no cause to do so. This is not true. Take consumption for example. Looking at the (non-filtered) time series for UK and US consumption, it is difficult to avoid attaching significant importance to the gradual evolution of credit conditions over the last two or three decades (see the references to work by Carroll and Muellbauer I give in this post). If this kind of work had received greater attention (which structural econometric modellers would almost certainly have done), that would have focused minds on why credit conditions changed, which in turn would have addressed issues involving the interaction between the real and financial sectors. If that had been done, macroeconomics might have been better prepared to examine the impact of the financial crisis.

It is not just Keynesian economics where reform rather than revolution might have been more productive as a consequence of Lucas and Sargent, 1979.

[1] The point is not whether expectations are generally rational or not. It is that any business cycle theory that depends on irrational inflation expectations appears improbable. Do we really believe business cycles would disappear if only inflation expectations were rational? PhDs of the 1970s and 1980s understood that, which is why most of them rejected the traditional Keynesian position. Also, as Paul Krugman points out, many Keynesian economists were happy to incorporate New Classical ideas. 

Rereading Lucas and Sargent 1979

Mainly for macroeconomists and those interested in macroeconomic thought

Following this little interchange (me, Mark Thoma, Paul Krugman, Noah Smith, Robert Waldman, Arnold Kling), I reread what could be regarded as the New Classical manifesto: Lucas and Sargent’s ‘After Keynesian Economics’ (hereafter LS). It deserves to be cited as a classic, both for the quality of ideas and the persuasiveness of the writing. It does not seem like something written 35 ago, which is perhaps an indication of how influential its ideas still are.

What I want to explore is whether this manifesto for the New Classical counter revolution was mainly about stagflation, or whether it was mainly about methodology. LS kick off their article with references to stagflation and the failure of Keynesian theory. A fundamental rethink is required. What follows next is I think crucial. If the counter revolution is all about stagflation, we might expect an account of why conventional theory failed to predict stagflation - the equivalent, perhaps, to the discussion of classical theory in the General Theory. Instead we get something much more general - a discussion of why identification restrictions typically imposed in the structural econometric models (SEMs) of the time are incredible from a theoretical point of view, and an outline of the Lucas critique.

In other words, the essential criticism in LS is methodological: the way empirical macroeconomics has been done since Keynes is flawed. SEMs cannot be trusted as a guide for policy. In only one paragraph do LS try to link this general critique to stagflation:

“Though not, of course, designed as such by anyone, macroeconometric models were subjected to a decisive test in the 1970s. A key element in all Keynesian models is a trade-off between inflation and real output: the higher is the inflation rate, the higher is output (or equivalently, the lower is the rate of unemployment). For example, the models of the late 1960s predicted a sustained U.S. unemployment rate of 4% as consistent with a 4% annual rate of inflation. Based on this prediction, many economists at that time urged a deliberate policy of inflation. Certainly the erratic ‘fits and starts’ character of actual U.S. policy in the 1970s cannot be attributed to recommendations based on Keynesian models, but the inflationary bias on average of monetary and fiscal policy in this period should, according to all of these models, have produced the lowest unemployment rates for any decade since the 1940s. In fact, as we know, they produced the highest unemployment rates since the 1930s. This was econometric failure on a grand scale.”

There is no attempt to link this stagflation failure to the identification problems discussed earlier. Indeed, they go on to say that they recognise that particular empirical failures (by inference, like stagflation) might be solved by changes to particular equations within SEMs. Of course that is exactly what mainstream macroeconomics was doing at the time, with the expectations augmented Phillips curve.

In the schema due to Lakatos, a failing mainstream theory may still be able to explain previously anomalous results, but only in such a contrived way that it makes the programme degenerate. Yet, as Jesse Zinn argues in this paper, the changes to the Phillips curve suggested by Friedman and Phelps appear progressive rather than degenerate. True, this innovation came from thinking about microeconomic theory, but innovations in SEMs had always come from a mixture of microeconomic theory and evidence. 

This is why LS go on to say: “We have couched our criticisms in such general terms precisely to emphasise their generic character and hence the futility of pursuing minor variations within this general framework.” The rest of the article is about how, given additions like a Lucas supply curve, classical ‘equilibrium’ analysis may be able to explain the ‘facts’ about output and unemployment that Keynes thought classical economics was incapable of doing. It is not about how these models are, or even might be, better able to explain the particular problem of stagflation than SEMs.

In their conclusion, LS summarise their argument. They say:

“First, and most important, existing Keynesian macroeconometric models are incapable of providing reliable guidance in formulating monetary, fiscal and other types of policy. This conclusion is based in part on the spectacular recent failures of these models, and in part on their lack of a sound theoretical or econometric basis.”

Reading the paper as a whole, I think it would be fair to say that these two parts were not equal. The focus of the paper is about the lack of a sound theoretical or econometric basis for SEMs, rather than the failure to predict or explain stagflation. As I will argue in a subsequent post, it was this methodological critique, rather than any superior empirical ability, that led to the success of this manifesto.

More on the illusion of superiority

For economists, and those interested in methodology

Tony Yates responds to my comment on his post on microfoundations, but really just restates the microfoundations purist position. (Others have joined in - see links below.) As Noah Smith confirms, this is the position that many macroeconomists believe in, and many are taught, so it’s really important to see why it is mistaken. There are three elements I want to focus on here: the Lucas critique, what we mean by theory and time.

My argument can be put as follows: an ad hoc but data inspired modification to a microfounded model (what I call an eclectic model) can produce a better model than a fully microfounded model. Tony responds “If the objective is to describe the data better, perhaps also to forecast the data better, then what is wrong with this is that you can do better still, and estimate a VAR.” This idea of “describing the data better”, or forecasting, is a distraction, so let’s say I want a model that provides a better guide for policy actions. So I do not want to estimate a VAR. My argument still stands.

But what about the Lucas critique? Surely that says that only a microfounded model can avoid the Lucas critique. Tony says we might not need to worry about the Lucas critique if policy changes are consistent with what policy has done in the past. I do not need this, so let’s make our policy changes radical. My argument still stands. The reason is very simple. A misspecified model can produce bad policy. These misspecification errors may far outweigh any errors due to the Lucas critique. Robert Waldmann is I think making the same point here. (According to Stephen Williamson, even Lucas thinks that the Lucas critique is used as a bludgeon to do away with ideas one doesn't like.)

Stephen thinks that I think the data speaks directly to us. What I think is that the way a good deal of research is actually done involves a constant interaction between data and theory. We observe some correlation in the data and think why that might be. We get some ideas. These ideas are what we might call informal theory. Now the trouble with informal theory is that it may be inconsistent with the rest of the theory in the model - that is why we build microfounded models. But this takes time, and in the meantime, because it is also possible that the informal theory may be roughly OK, I can incorporate it in my eclectic model.[1] In fact we could have a complete model that uses informal theory - what Blanchard and Fischer call useful models. The defining characteristic of microfounded models is not that they use theory, but that the theory they use can be shown to be internally consistent.

Now Tony does end by saying “ad-hoc modifications seem attractive if they are a guess at what a microfounded model would look like, and you are a policymaker who can’t wait, and you find a way to assess the Lucas-Critique errors you might be making.” I have dealt with the last point – it’s perfectly OK to say the Lucas critique may apply to my model, but that is a price worth paying to use more evidence than a microfounded model does to better guide policy. For the sake of argument let’s also assume that one day we will be able to build a microfounded model that is consistent with this evidence. (As Noah says, I’m far too deferential, but I want to persuade rather than win arguments.) [2] In that case, if I’m a policy maker who cannot wait for this to happen, Tony will allow me my eclectic model.

This is where time comes in. Tony’s position is that policymakers in a hurry can do this eclectic stuff, but we academics should just focus on building better microfoundations. There are two problems with this. First, building better microfoundations can take a very long time. Second, there is a great deal that academics can say using eclectic, or useful, models.

The most obvious example of this is Keynesian business cycle theory. Go back to the 1970s. The majority of microfoundations modellers at that time, New Classical economists, said price rigidity should not be in macromodels because it was not microfounded. I think Tony, if he had been writing then, would have been a little more charitable: policymakers could put ad hoc price rigidities into models if they must, but academics should just use models without such rigidities until those rigidities could be microfounded.

This example shows us clearly why eclectic models (in this case with ad hoc price rigidities) can be a far superior guide for policy than the best microfounded models available at the time. Suppose policymakers in the 1970s, working within a fixed exchange rate regime, wanted to devalue their currency because they felt it had become overvalued after a temporary burst of domestic inflation. Those using microfounded models would have said there was no point - any change in the nominal exchange rate would be immediately offset by a change in domestic prices. (Actually they would probably have asked how the exchange rate can be overvalued in the first place.) Those using eclectic models with ad hoc price rigidities would have known better. Would those eclectic models have got things exactly right? Almost certainly not, but they would have said something useful, and pointed policy in the right direction.

Should academic macroeconomists in the 1970s have left these policymakers to their own devices, and instead got on with developing New Keynesian theory? In my view some should have worked away at New Keynesian theory, because it has improved our understanding a lot, but this took a decade or two to become accepted. (Acceptance that, alas, remains incomplete.) But in the meantime they could also have done lots of useful work with the eclectic models that incorporated price stickiness, such as working out what policies should accompany the devaluation. Which of course in reality they did: microfoundations hegemony was less complete in those days.

Today I think the situation is rather different. Nearly all the young academic macroeconomists I know want to work with DSGE models, because that is what gets published. They are very reluctant to add what might be regarded as ad hoc elements to these models; however strong the evidence and informal theory might be that could support any modification. They are also understandably unclear about what counts as ad hoc and what does not. The situation in central banks is not so very different.

This is a shame. The idea that the only proper way to do macro that involves theory is to work with fully microfounded DSGE models is simply wrong. I think it can distort policy, and can hold back innovation. If our DSGE models were pretty good descriptions of the world then this misconception might not matter too much, but the real world keeps reminding us that they are not. We really should be more broad minded. 

[1] Suppose there is some correlation in the past that appears to have no plausible informal theory that might explain it. Including that in our eclectic model would be more problematic, for reasons Nick Rowe gives.

[2] I suggest why this might not be the case here. Nick Rowe discusses one key problem, while comments on my earlier post discuss others.

The Lucas Critique and Internal Consistency

For those interested in microfoundations macro. Unlike earlier posts, I make no judgement about the validity or otherwise of the microfoundations approach, but instead just try and clarify two different motivations behind microfoundations.

When I discuss the microfoundations project, I say that internal consistency is the admissibility criteria for microfounded models. I am not alone in stressing the role of internal consistency: for example in the preface to their highly acclaimed macroeconomics textbook, Obstfeld and Rogoff (1996) argue that a key problem with the pre microfoundations literature is that it “lacks the microfoundations needed for internal consistency”. However when others talk about microfoundations, they often say they are designed to avoid the Lucas critique. This post argues that the latter is just a particular case of the former.

What do we mean when we say a model is internally consistent? Most obviously, we mean that individual agents within the model behave consistently in making their own decisions. A trivial example is if the model contains a labour supply equation and a consumption function that are supposed to represent the behaviour of the same agent. In that case we would want the agent to behave consistently. An agent that became more impatient, and so wanted to consume more by borrowing, but also wanted to work more hours (and so exhibit less impatience in their consumption of leisure), would appear to behave inconsistently unless their preferences or prices also changed.

Suppose instead of a labour supply equation, we had wage setting by unions. In this case we have a consistency issue between two sets of agents: consumers and unions. If we wanted to model unions as representing consumers as workers, we would want to align their preferences, so we are back to the previous case. However, there may be reasons why we do not want to do this. If we did not, we would want to make sure these agents interrelated in a sensible way.

What is meant by a sensible way? Consumer’s decisions will almost certainly depend on expectations about the wages unions set. Lucas called rational expectations a ‘consistency axiom’. If, for example, the union started being more concerned about employment than wages, we might expect consumers to recognise this in thinking about how their future income might evolve.

The Lucas critique is just an example of consistency between agents. The question is whether the private sector agents in the model react in a sensible way to policy changes. The classical example of the Lucas critique is inflation expectations. If monetary policy changes to become much harder on inflation, then rational agents will incorporate that into the way they form inflation expectations. A model that did not have that feedback would be ‘subject to the Lucas critique’.

Discussion of the Lucas critique often involves the need to model in terms of ‘deep’ parameters. A deep parameter (like impatience) is one that is independent of (exogenous to) the rest of the model. Here the parameters of the rule agents’ use to forecast inflation are not deep parameters, because (under rational expectations) they depend on how policy is made. But we can have a similar discussion about workers and unions: if the latter aimed at representing the former, then union attitudes to the wage/employment trade off should not be independent of worker preferences. Internal consistency is again more general than the Lucas critique.

Now obviously the Lucas critique is a particularly important kind of inconsistency if you are interested in analysing policy. But it is not the only kind of inconsistency that matters. A very good example of this is Woodford’s derivation of a social welfare function from the utility function of agents. Before this work, macroeconomists had typically assumed that a benevolent policy maker would minimise some quadratic combination of excess inflation and output, but this was disconnected from consumers’ utility. This had no bearing on the Lucas critique, which applies to any policy, benevolent or not. However it was a glaring example of inconsistency – why wasn’t the policy maker maximising the representative agent’s utility? After Woodford’s analysis, nearly every macroeconomics paper followed his example: not because it did anything about the Lucas critique, but because it solved an internal consistency issue.

Why does putting the Lucas critique in its proper place matter? I can think of two reasons. First, if you believe that avoiding the Lucas critique means you necessarily have a microfounded model, you are wrong. (In contrast, an internally consistent model will avoid the Lucas critique.) Second, it has a bearing on the idea often put forward that microfounded models are just for policy analysis, but not for forecasting. If we think that microfoundations is all about the Lucas critique, then this mistake is understandable (although still a mistake). But if microfoundations is about internal consistency, then it is easier to see how a microfounded model could be much better at forecasting as well as policy analysis.

Some notes on macro modelling

             This post is prompted by this post by Robert Waldmann, and this by Noah Smith, commenting on an earlier post by Wieland and Wolters. 

1) Forecasting and policy analysis

Noah repeats what is a standard line, which is that microfounded models are for policy analysis and not forecasting, and for forecasting “we don't need the structural [microfounded] models, and might as well toss them out”. The reason he gives is policy invariance: microfounded models address the Lucas critique.

While the Lucas critique is important, it is not in my view the reason we have microfounded models. The need for internal consistency drives the microfoundations project. Often internal consistency and addressing the Lucas critique go together, but not always. The clearest example is Woodford’s derivation of a quadratic social welfare function from agents’ utility. This is not needed to address the Lucas critique, but it is required for an internally consistent analysis of what a benevolent policy maker should do.

Why is internal consistency important? Because we think that agents in the real world are internally consistent, so models that are not can make mistakes. They can make mistakes in forecasting as well as policy analysis.

However, in an effort to achieve internal consistency, we may well ignore important features of the real world. ‘Ad hoc’ models that capture these features may be better models, and give better policy advice, even though they are potentially internally inconsistent.

So microfounded models could be better at forecasting, and ‘ad hoc’ models could give better policy advice. In that sense I think Noah is repeating a common misperception.

2) On a pedantic point, there is a long tradition of comparing different macromodels, both for forecasting and policy analysis, so Wieland and Wolters is hardly a first step. In the UK for 16 years we had an excellent research centre that did just that, run by Ken Wallis. There is a wealth of expertise there, which anyone doing this kind of comparative analysis needs to tap.

3)  Just in case anyone reading Robert’s post gets the wrong impression, the idea of the core/periphery structure for the Bank of England’s model came from economists at the Bank (strongly influenced by the antecedents from other central banks that I mentioned in my post), and not me. My role was mainly to give advice on theoretical aspects of the model to a very competent team who needed little of it. However Robert and Noah are wrong to suggest that because the Bank uses the core/periphery structure for forecasting, there is no point in having the microfounded core. For example, you can do policy analysis with both the complete model and just the core.

4) This final comment is just for those who read Robert’s post, and is very pedantic. Robert starts off by saying “As far as I can tell, Simon Wren-Lewis has been convinced by Paul Krugman”. The first point is that all my posts on this issue have come from a consistent view. I think microfoundations modelling is an important thing to do, but I do not think it is the only valid way of modelling the economy and doing policy analysis. I think Paul Krugman and I are on absolutely the same page here, and always have been. Robert is however right that my aim has been to convince those doing microfounded modelling of this point.

I’ve disagreed with Paul Krugman (and Robert) on the empirical success of the microfoundations approach, and I still disagree. But given that we agree that analysing microfounded models is useful, I don’t think this is terribly important. I picked up on the ‘mistaking beauty for truth’ phrase, because – taken literally – I don’t think that this is a problematic force behind the way the microfoundations project progresses. All scientists like simplicity, and they also get complicated when they need to, and DSGE models do the same. What I think is problematic is the weak role played by external consistency that I illustrated here, and the role of ideology. On the latter I think I’m once again on the same page as Paul Krugman.