Via Economist' View: Ein interessantes Interview mit James Bullard, dem Präsidenten der St. Louis Fed.
In diesem geht es um das Verhältnis von akademischer ökonomischer Forschung und der Arbeitsweise von Ökonomen im politischen Betrieb. Auszüge:
"I have been dissatisfied with the notion that has evolved over the last 25 or 30 years that it was okay to allow a certain group of economists to work on really rigorous models and do the hard work of publishing in journals and then have a separate group that did policymaking and worried about policymaking issues. These two groups often did not talk to each other, and I think that that is a mistake. [...]I am not one who thinks that the issues discussed in the academic journals are just navel gazing. Those are our core ideas about how the economy works and how to think about the economy. There are no better ideas. That is why they are published in the leading journals. So I do not think you should ignore those. Those ideas should be an integral part of the thinking of any policymaker. I do not think that you should allow policymaking to be based on a sort of second-tier analysis. [...]I think one thing about macroeconomics is that because everyone lives in the economy and they talk to other people who live in the economy, they think that they have really good ideas about how this thing works and what we need to do. I do not begrudge people their opinions, but when you start thinking about it, it is a really complicated problem. I love that about macroeconomics because it provides for an outstanding intellectual challenge and great opportunities for improvement and success. I do not mind working on something that is hard. But everyone does seem to have an opinion. In medicine you do see some of that: People think they know better than the doctors and they think they are going to self-medicate because their theory is the right one, and the doctors do not know what they are doing. [...]One of the main things about becoming a policymaker is the juxtaposition between the role of forecasting and the role of modeling to try to understand how better policy can be made. In the policy world, there is a very strong notion that if we only knew the state of the economy today, it would be a simple matter to decide what the policy should be. The notion is that we do not know the state of the system today, and it is all very uncertain and very hazy whether the economy is improving or getting worse or what is happening. Because of that, the notion goes, we are not sure what the policy setting should be today. So, the idea is that the state of the system is very hard to discern, but the policy problem itself is often disarmingly simple. What is making the policy problem hard is discerning the state of the system. That kind of thinking is one important focus in the policy world. In the research world, it is just the opposite. The typical presumption is that one knows the state of the system at a point in time. There is nothing hazy or difficult about inferring the state of the system in most models. However, the policy problem itself is often viewed as really difficult. It might be the solution to a fairly sophisticated optimization problem that carefully weighs the effects of the policy choice on the incentives of households and firms in a general equilibrium context. That kind of attitude is just the opposite of the way the policy world approaches problems. [...][...] there is no alternative to structural models. We are trying to get policy advice out of the models; at the end of the day, we are going to have to have a structural model. We have learned a lot about how to handle data and how to use statistical techniques for many purposes in the field, and I think those are great advances. These days you see a lot of estimation of DSGE models, so that is a combination of theorizing with notions of fit to the data. I think those are interesting exercises. I do not really see this as being two branches of the literature. There is just one branch of the literature. There may be some different techniques that are used in different circumstances. Used properly, you can learn a lot from purely empirical studies because you can simply characterize the data in various ways and then think about how that characterization of the data would match up with different types of models. [...]"
Gegen Ende des Interviews spricht er über den Bedarf für große makroökonomische Modelle, die gewissermaßen alle Features der Volkswirtschaft abbilden. Bullard ist da sehr positiv eingestellt. Ich hingegen glaube, dass dies - wie in den 1970ern - nicht der Weisheit letzter Schluss sein wird.
"I have argued that the research effort in the U.S. and around the world in economics needs to be upgraded and needs to be taken more seriously in the aftermath of the crisis. I think we are beyond the point where you can ask one person or a couple of smart people to collaborate on a paper and write something down in 30 pages and make a lot of progress that way. At some point the profession is going to have to get a lot more serious about what needs to be done. You need to have bigger, more elaborate models that have many important features in them, and you need to see how those features interact and understand how policy would affect the entire picture. A lot of what we do in the published literature and in policy analysis is sketch ingenious but small arguments that might be relevant for the big elephant that we cannot really talk about because we do not have a model of the big elephant. So we only talk about aspects of the situation, one aspect at a time. Certainly, being very familiar with research myself and having done it myself, I think that approach makes a great deal of sense. As researchers, we want to focus our attention on problems that can be handled and that one can say something about. That drives a lot of the research. But in the big picture, that is not going to be enough in the medium run or the long run for the nation to get a really clear understanding of how the economy works and how the various policies are affecting the macroeconomic outcomes. We should think more seriously about building larger, better, more encompassing types of models that put a lot of features together so that we can understand the relative magnitudes of various effects that we might think are going on all at the same time. We should also do this within the DSGE context, in which preferences are well specified and the equilibrium is well defined. Therein lies the conflict: to get to big models that are still going to be consistent with micro foundations is a difficult task. In other sciences you would ask for a billion dollars to get something done and to move the needle on a problem like this. We have not done that in economics. We are way too content with our small sketches that we put in our individual research papers. I do not want to denigrate that approach too much because I grew up with that and I love that in some sense, but at some point we should get more serious about this. One reason why this has not happened is that there were attempts in the past (circa 1970) to try to put together big models, and they failed miserably because they did not have the right conceptual foundations about how you would even go about doing this. Because they failed, I think that has made many feel like, "Well, we are not going to try that again." But just because it failed in the past does not mean it is always going to fail. We could do much better than we do in putting larger models together that would be more informative about the effects of various policy actions without compromising on our insistence that our models be consistent with microeconomic behavior and the objects that we study are equilibrium outcomes under the assumptions that we want to make about how the world works."