As far as I
can could tell, Simon Wren-Lewis has been convinced by Paul Krugman. He now proposes parallel reasearch projects one of which is to be focused on fitting the data. This is exactly what Krugman advocated.
Update: clearly I couldn’t tell very far. In fact, as he has repeatedly written, Wren-Lewis always agreed with Krugman about what is to be done. I assume he still disagrees with Krugman about the fruits of the effort to micro found macro. In any case, I missinterpreted his new parallel research program proposal. It is the same as his original paralkel research program proposal.
Also he isn’t the one who caused the Bank of England model to have an ad hoc periphery around the consistent core. That was the work of Bank of England employees. Or something. Just go to his blog for more reliably correct corrections.
In the real world the incentive for most academics is to get publications, often within a limited time frame. When the focus of macroeconomic analysis is on internal consistency rather than external consistency, then it is unclear whether this incentive mechanism is socially optimal. If it is not, then one solution is for all macroeconomists to work for central banks! A more realistic alternative might be to reprise within academic macroeconomics a modelling tradition which placed more emphasis on external consistency and less on internal consistency, to work alongside the microfoundations approach.
My reading is that Wren Lewis has ceased to present the empirical research as subordinate to micro founded modeling. He seems to accept a research project which developes on its own and not just as an indication for what should be added to consistent models including agents with rational expectations. Wren-Lewis seems to have only a verbal quibble with Krugman who says the fashion was for models which are beautiful not true. Wren Lewis notes that many of the models are ugly. I’d consider the debate resolved essentially with Wren Lewis saying he is convinced. I note that he started arguing for some role for non-micro founded models. My guess is that, when the debate started, he thought what he wrote this time, but was trying to appeal to the profession which considers the micro founded models to be the only research of scientific value.
I can’t let go of the debate however. In the post to which I link above, Wren Lewis links to another post in which he explains how he really modelled when advising the Bank of England. It turns out that he didn’t stick to micro founded models at all. There was a “core model” which is logically consistent and features rational agents. But the output of this model is just one input to ad hoc models used for actual forecasting.
If we take a microfounded model to the data, what we invariably find is that the errors for any particular aggregate relationship are not just serially correlated (if the equation overpredicts today, we know something about the error it will make tomorrow) but also systematically related to model variables. If the central bank ignores this, it will be throwing away important and useful information. Take forecasting. If I know, say, that the errors in a microfounded model’s equation for consumption are systematically related to unemployment, then the central bank could use this knowledge to better predict future consumption.
BEQM addressed this problem by splitting the model into two: a microfounded ‘core‘, and an ad-hoc ‘periphery’. The periphery equation for consumption would have the microfounded model’s prediction for consumption on the right hand side, but other variables like unemployment (and lags) could be added to get the best fit with the data. However this periphery equation for consumption would not feed back into the microfounded core. The microfounded core was entirely self-contained: to use a bit of jargon, the periphery was entirely recursive to the core.
The hybrid model is not logically consistent (wealth evolves as if consumption were determined by the core model, but forecast consumption is different so budget constraints are not satisfied with equality in the hybrid model).
The interesting thing to me is that the example of core plus ad hoc was the model used to forecast consumption. Some time ago when I challenged Wren Lewis to present a case in which micro founded models were useful in forecasting, he brought up consumption and noted that an old Keynesian model used by the Bank of England forecast lower than observed consumption. Now he notes that the model which is currently used by the Bank of England for that very variable is an ad hoc internally inconsistent model. This was the example he chose when defending micro founded models.
Now he notes that the micro founded model might in the future be improved so that it gives useful forecasts (he mentions precautionary savings) but “might” makes right. Such a claim can be made for any resarch program no matter how sterile.
I comment with my usual rudeness. Just to be quick, the example shows that the Bank of England does not, in fact, use a micro founded model. The model is written down, but it is over ridden when making forecasts. In my comment I ask if there is any hint of any useful role of the “core model”.
I note that you provide neither evidence nor reasoning in support of the claim that Microfounded model building is a very important and useful thing to do.”. Notably the core-periphery hybrid model is vulnerable to the Lucas crtique. What does the core model add which is useful ? Having a model which is not confronted with the data written down adds what exactly ? Does the core-periphery model fit the data better than a model which is not based on adding inconsistent equations to a logically consistent core ?
You note someone’s (King’s I assume) determination to include a logically consistent core ( then add inconsistent equations before making predictions or guiding policy. As presented, this seems to be a matter of intellectual fashion. Evidence doesn’t appear in that stage of your story (as it does in the stage of adding ad hoc corrections to thecore model).
I don’t know where to put this, but Mankiw’s use of the word ” scientists” has no connection with practice in the natural sciences in which, historically, theories bow to facts. Can he explain why he did’t write “mathematicians” ?
Finally, what basis is there for the claim that the “better journals” are better than other journals ? They are presented as journals publishing social science, but empirical success is not required. You note correctly that they have huge status in the field, but do not address the question of whether this has anything to do with science, reality, understanding, insight or intellectual progress.
Oh I am getting rude as usual. So, before hitting publish, I want to thank you on behalf of at least UK residents for not sticking with an elegant model which does not fit the data. As I understand this post you et al rendered a false but fashionable model harmless. This made the UK a better place and I applaud your intellectual courage.