Take a typical consumption function where consumption depends on current income and other things. Income is endogenous. In CC models using 2SLS, first stage regressors might include variables like government spending and tax rates, possibly lagged one quarter. Also, lagged endogenous variables might be used like lagged investment. If the error term in the consumption equation is serially correlated, it is easy to get rid of the serial correlation by estimating the serial correlation coefficients along with the structural coefficients in the equation. So assume that the remaining error term is iid. This error term is correlated with current income, but not with the first stage regressors, so consistent estimates can be obtained. This would not work and the equation would not be identified if all the first stage regressors were also explanatory variables in the equation, which is the identification criticism. However, it seems unlikely that all these variables are in the equation. Given that income is in the equation, why would government spending or tax rates or lagged investment also be in? In the CC framework, there are many zero restrictions for each structural equation, and so identification is rarely a problem. Theory rules out many variables per equation.
Pointer from Mark Thoma.
I am afraid that Ray Fair leaves out the main reason that I dismiss macroeconometric models, namely the “specification search” problem. As you can gather from the quoted paragraph, there are many ways to specify a macroeconometric model. Fair and other practitioners of his methods will try dozens of specifications before settling on an equation. As Edward Leamer pointed out in his book on specification searches, this process destroys the scientific/statistical basis of the model.
I have much more to say on this issue, both in my Science of Hubris paper and in my Memoirs of a Would-be Macroeconomist. In the latter, I recount the course that I took with Fair when he was a visiting professor at MIT.
1. On DSGE, I think that the main vice is the “representative agent” consolidation. It completely goes against the specialization and trade way of thinking. Fighting the whole “representative agent” modeling approach is a major point of the Book of Arnold, or at least it is supposed to be. (I may have been too terse in the macro section of my first draft.)
2. VAR models are just a stupid waste of time. As I said in a previous post, we do not have the luxury of saying that we construct models that correspond with reality. What models do is allow us to describe what a possible world would look like, given the assumptions that are built into it. VAR models do not build in assumptions in any interesting way. That is claimed to be a feature, but in fact it is a huge bug.
I think that the project of building a model of the entire economy is unworkable, because the economy as whole consists of patterns of specialization and trade that are too complex to be captures in a model. But if you forced me to choose between VAR, DSGE, and the old-fashioned stuff Fair does, I would actually use that. At least his model can be used to make interesting statements about the relationship of assumptions to predicted outcomes. But that is all it is good for, and for my money you are just as well off making up something on the back of an envelope.