Real-Time Economic Analysis
When Narayana Kocherlakota gave this speech based on this paper, a paper that uses a very simply model that is essentially an IS curve analysis, the economists who believe strongly in the science of monetary policy were appalled. How could Narayana have crossed over to the dark side?
I defended him, and it leads me into a broader discussion of the problems of doing what I've called "real-time economic analysis." Let me start with something I wrote about this awhile back:
Economic research is largely backward looking. After the fact – when all of the data has been collected and the revisions to the data are complete – economists examine data on, say, a financial crisis, and then figure out what caused the economy to become so sick. Once the cause has been determined, which may involve the construction of new theoretical frameworks, they tell us how to avoid it happening again, i.e. the particular set of policies that would have prevented or attenuated the damage.
But the internet and blogs are changing what we do, and to some extent we now act like emergency room physicians rather than pathologists who have the time to carefully examine data from tests, etc., determine what went wrong, and then recommend how to avoid problems in the future. When the financial crisis hit so unexpectedly, it was like a patient showed up at the emergency room very sick and in need of immediate diagnosis and care. We had to reach into our bag of macroeconomic models, choose the one that was correct for this question, and then use it to both diagnose the problems and prescribe policies to fix them. There was no time for a careful retrospective analysis that patiently determined the cause and then went to work on the potential policy responses.
That turned out to be much harder than expected. Our models and cures are not designed for that type of use. What data should we look at to make an immediate diagnosis? What tests should we conduct to give us data on what is wrong with the economy? If we aren’t sure what the cause is but immediate action is needed to save the economy from getting very sick, what is the equivalent of using broad spectrum antibiotics and other drugs to attack unknown problems? The development of blogs puts economists in real-time contact with the public, press, and policymakers, and when a crisis hits, traffic spikes as people come looking for answers.
Blogs are a start to solving the problem of real-time analysis, but we need to do a much better job than we are doing now at providing immediate answers when they are needed. If Lehman is failing and the financial sector is going down with it, or if Europe is in trouble, we need to know what to do right now. It won’t help to figure that out months from now and then publish the findings in a journal article. That means the discipline has to adjust from being backward looking pathologists with plenty of time to determine causes and cures to an emergency room mode where we can offer immediate advice. Blogs are an integral part of that process.
Policymakers at the Federal Reserve face this problem continuously. They must confront changes in the data that aren't always well understood in near real time, and make policy decisions every few weeks. If pre-existing models apply to the problem at hand, great, it can be used to guide policy decisions. But what should policymakers do when they are faced with an important decision about how to react to a large shock, and they reach into their black bag of models and none of them seem to fit?
One approach is what Paul Krugman does so well, something Narayana Kocherlakota seems to also be doing. Reach for simple models that get to the heart of the problem and hence offer guidance about what to do next. These models are not intended to explain the world generally, they are not "science" in that respect, they are intended to shine a light and provide guidance on a very narrow issue. It takes considerable skill to do this since, as I argued yesterday, it requires the practitioner to thoroughly understand the pitfalls of the simple approach, the ways in which it could go wrong.
So I think Narayana and others are correct to reach for simple models for guidance when they are faced with a decision that existing models do not address very well and there's not time to build a full-blown model of the problem.
My call to those who object that this approach is not "science," those who look down their noses at people like Krugman and Kocherlakota when they adopt this approach, is this. What is the scientific way to diagnose the economy is real-time, and confront unknown or uncertain pathologies? As I noted in another essay that discusses this problem, doctors have tests that can be done very quickly to provide a diagnosis, and they can they use broad-spectrum drugs and other approaches to try to heal the patient when the tests point to unknown causes.
What tests should we do that are quick and informative? There are lots of data, but what should we be examining to try to diagnose problems effectively before they get really bad? If we detect a problem, and don't fully understand it, what's the most robust way to attack it? What policies tend to work on a broad variety of underlying causes? Are there tests that can guide us to the correct robust policy?
My reaction when the crisis hit, and ever since, was to recommend a "portfolio of policies." People who say only monetary policy will work, or only fiscal policy will work, blah, blah, blah are talking with more confidence than was justified by the models they are using. I decided early on that I really didn't know for sure which macroeconomic model was best. I had my preferences, strong preferences, but I couldn't say for sure that the model I preferred was correct. And it didn't really apply very well to the financial crisis in any case.
So, I thought, why not do what a doctor would do and give a broad spectrum drug that tends to work no matter the cause. There is the danger of side effects. If we aren't sure about which policy will work and we give full doses of both monetary and fiscal policy only to have them both work, the side effect of inflation could occur as the economy heals. But to me the side effect was far less worrisome than the disease itself, and in any case the side effect could be controlled by backing off the dosage once the patient was up and about once again. But what are the optimal weights for monetary and fiscal policy in such a situation? What else ought to be in the portfolio of policies (e.g. policies that can help even if the problem is structural rather than cyclical). What guidance can we give policymakers?
Those who believe in the science of monetary policy can sneer at the Krugman/Kocherlakota approach all they want, but there's a real (time) problem to be solved here and we could use their help. As I said above this is an area where the Fed has considerable experience, real-time analysis is a large part of what they do, and my push for Federal Reserve banks to interact more through blogs is partly for this reason. Hearing how Federal Reserve policymakers approach these problems would be useful.
But it would also be useful if the profession more generally would get aboard and help us understand how to better solve the difficult questions that arise when decisions must be made based upon only a partial understanding of the problem that is affecting the economy. In the long-run it's still important to build new, full blown models that can explain the problem and provide guidance. Macroeconomists are certainly doing that presently as they try to provide better models of how a breakdown in financial intermediation can impact the real economy than we had before, and so on. But work on how to better conduct real-time analysis is not getting as much attention, and that's something that needs to change.
Posted by Mark Thoma on Sunday, April 1, 2012 at 09:27 AM in Economics, Fiscal Policy, Macroeconomics, Methodology, Monetary Policy |
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