As I see it, the main characteristic of "equilibrium" models Lucas and Sargent inaugurated is that they put people, time, and economics into macro.A Bloomberg View column by Noah Smith nicely summarizes the methodological shift, which gained momentum from the apparent breakdown of the Phillips curve relationship between inflation and unemployment in the 1970s. Smith writes:
Keynesian models model aggregates. Consumption depends on income. Investment depends on interest rates. Labor supply and demand depend on wages. Money demand depends on income and interest rates. "Consumption" and "investment" and so forth are the fundamental objects to be modeled.
"Equilibrium" models (using Lucas and Sargent's word) model people and technology. People make simultaneous decisions across multiple goods, constrained by budget constraints -- if you consume more and save more, you must work more, or hold less money. Firms make decisions across multiple goods constrained by technology.
Putting people and their simultaneous decisions back to the center of the model generates Lucas and Sargent's main econometric conclusion -- Sims' "incredible" identifying restrictions. When people simultaneously decide consumption, saving, labor supply, then the variables describing each must spill over in to the other. There is no reason for leaving (say) wages out of the consumption equation. But the only thing distinguishing one equation from another is which variables get left out.
People make decisions thinking about the future. I think "static" vs. "intertemporal" are good words to use. That observation goes back to Friedman: consumption depends on permanent income, including expected future income, not today's income. Decisions today are inevitably tied to expectations --rational or not -- about the future.
Lucas showed that trying to boost gross domestic product by raising inflation might be like the tail trying to wag the dog. To avoid that kind of mistake, he and his compatriots declared, macroeconomists needed to base their models on things that wouldn’t change when government policy changed -- things like technology, or consumer preferences. And so DSGE was born. (DSGE also gave macroeconomists a chance to use a lot of cool new math tricks, which probably increased its appeal.)That's an interesting question -- when thinking about issues like this, I often come back to the divide between "science" and "engineering" put forward by Greg Mankiw. While academic macroeconomics has gone down the path marked out Lucas and Sargent, the policymaking "engineers" in Washington often still find the older-style models more useful. It sounds like Wall Street's economists do too.
OK, history lesson over. So why is this important now?
Well, for one thing, the finance industry has ignored DSGE models. That could be a big mistake! Suppose you’re a macro investor. If all you want to do is make unconditional forecasts -- say, GDP next quarter – then you can go ahead and use an old-style SEM model, because you only care about correlation, not causation. But suppose you want to make a forecast of the effect of a government policy change -- for example, suppose you want to know how the Fed’s taper will affect growth. In that case, you need to understand causation -- you need to know whether quantitative easing is actually changing people’s behavior in a predictable way, and how.
This is what DSGE models are supposed to do. This is why academic macroeconomists use these models. So why doesn’t anyone in the finance industry use them? Maybe industry is just slow to catch on. But with so many billions upon billions of dollars on the line, and so many DSGE models to choose from, you would think someone at some big bank or macro hedge fund somewhere would be running a DSGE model. And yet after asking around pretty extensively, I can’t find anybody who is.
The question is whether academic macroeconomics is on track to produce models that are more useful for the policymakers and moneymakers. The DSGE method is still fairly new, and, until recently, we've been constrained by the limitations of our computers as well as our minds (a point Narayana Kocherlakota made here), so maybe we're just not quite there yet. But we should be open to the possibility that we're on the wrong track entirely.