The ingredients of DSGE models
Olivier Blanchard writes that DSGE models have come to play a dominant role in macroeconomic research. Some see them as the sign that macroeconomics has become a mature science, organized around a microfounded common core. Others see them as a dangerous dead end. Olivier Blanchard thinks that DSGEs should be the architecture in which the relevant findings from the various fields of economics are eventually integrated and discussed, but this is not the case today.
Timothy Taylor writes that DSGE models are a method that is both well established and the stuff of continuing controversy. Olivier Blanchard writes that DSGE models make three strategic modeling choices: First, the behavior of consumers, firms, and financial intermediaries, when present, is formally derived from microfoundations. Second, the underlying economic environment is that of a competitive economy, but with a number of essential distortions added, from nominal ties to monopoly power to information problems. Third, the model is estimated as a system, rather than equation by equation in the previous generations of macroeconomic models. Current DSGE models are best seen as large-scale versions of the New Keynesian model, which emphasizes nominal rigidities and a role for aggregate demand.
Olivier Blanchard writes that a typical DSGE paper adds a particular distortion to an existing core. It starts with an algebra-heavy derivation of the model, then goes through estimation, and ends with various dynamic simulations showing the effects of the distortion on the general equilibrium properties of the model.
Olivier Blanchard writes that a major potential strength of DSGE models is that they can be used not only for descriptive but also for normative purposes. The problem in practice is that the derivation of welfare effects depends on the way distortions are introduced in the model. And, often, for reasons of practicality, these distortions are introduced in ways that are analytically convenient but have unconvincing welfare implications. To take a concrete example, the adverse effects of inflation on welfare in these models depend mostly on their effects on the distribution of relative prices as not all firms adjust nominal prices at the same time. Research on the benefits and costs of inflation suggests, however, a much wider array of effects of inflation on activity and in turn on welfare.
Are ad hoc and DSGE models substitutes or complements?
Olivier Blanchard writes that models can also differ in their degree of simplicity. Not all models have to be explicitly microfounded. Ad hoc macro models, from various versions of the IS-LM to the Mundell-Fleming model, have an important role to play in relation to DSGE models. They can be useful upstream, before DSGE modeling, as a first cut to think about the effects of a particular distortion or a particular policy. They can be useful downstream, after DSGE modeling, to present the major insight of the model in a lighter and pedagogical fashion. The DSGE and the ad hoc models are complements, not substitutes.
Paul Krugman writes that a modeling approach is truly useful surprising if it generates surprising successful predictions. General relativity, for example, got its big boost when light did, in fact, bend as predicted. In recent years economists who still took seriously good old-fashioned Hicksian IS-LM type analysis made some strong predictions after the financial crisis that were very much at odds with what quite a few economists were saying. With interest rates near zero massive increases in the monetary base would not cause high inflation, large budget deficits would not drive interest rates up or crowd out private investment, and fiscal multipliers would be positive, in fact more than one, and would be considerably larger than estimates based on non-liquidity-trap episodes suggested.
Paul Krugman writes that the problem with DSGE models is that they not failed to provide surprising successful predictions but had the effect of crowding out the stuff that actually did work. Blanchard writes that he was allowed to present the basic insight more simply only because he was the discussant, but that the author of the paper wasn’t allowed to do the same thing. That’s DSGE substituting for, in fact, preventing the ad hoc approach.
Simon Wren-Lewis writes that what seems to be missing is a connection between people working on partial equilibrium analysis (like consumption) and general equilibrium modelers. Top journal editors’ preference for the latter means that the former is less highly valued. The failure, for example, to take seriously the strong evidence about the importance of changes in credit availability for consumption played an important part in the inability of macroeconomics to adequately model the response to the financial crisis. Even if you do not accept that, the failure of most DSGE models to include any kind of precautionary saving behaviour does not seem right when DSGE has a monopoly in ‘proper modeling’.
DSGE modeling as signaling
Noah Smith writes that assuming that DSGE models really don't work, why do so many macroeconomists spend so much time on them? One obvious hypothesis is that a huge percent of their human capital is already invested in knowing how to do this technique, so they just keep doing what they know how to do, and teaching it to their grad students. Another hypothesis could be that it's just an equilibrium of a repeated coordination game. Universities pay macroeconomists to do research, but they have absolutely no idea what good macroeconomic research is, so in practice they pay macroeconomists to do whatever other macroeconomists decide is good. Another hypothesis is signaling. Theory papers are getting much less common in top econ journals, but are still prominent among job market papers. What's being signaled, why is it valuable, and why is it hard to observe directly? The obvious possibility is that it's signaling intelligence - that the ability to make DSGE models is just an upper-tail IQ test. That's valuable because A) in the long run, people with very high intelligence are going to do good research, and B) intelligence gets much harder to observe in the upper tail.
Brad DeLong writes that if you insist on trying to understand business cycles by requiring a single consumption Euler equation (rather than, say, risk-averse rich 70-somethings with short horizons; myopic middle-class 40-somethings, and the liquidity constrained); if you insist on trying to understand business cycles by requiring that firms engage in Calvo pricing; If you insist on trying to understand business cycles by requiring rational expectations (rather than anchored, adaptive, extrapolative, perfect-foresight, and Panglossian) – well, then you really aren’t very bright at all, are you?