In a comment on this post, Noah Smith commended to me the work of George-Marios Angeletos of MIT. Unfortunately, Angeletos is fond of vector autoregressions (VARs), which I detest.
I got my start in macro working on structural macroeconometric models. I saw them close up, and I am keenly aware of the problems with them. Hence, I wrote Macroeconometrics: The Science of Hubris.
However, I will give the old-fashioned macroeconometricians credit for at least worrying about the details of the data they are using. If there are structural factors that are changing over time, such as trend productivity growth or labor force participation, the macroeconometrician will keep track of these trends. If there are special factors that change quarterly patterns, such as the “cash-for-clunkers” program that shifted automobile purchases around, the macroeconometrician will take these into account.
The VAR crowd cheerfully ignores all the details in macro data. The economist with a computer program that will churn out VARs is like a 25-year-old with a new immersion blender. He does not want to spend time cooking carefully-selected ingredients. He just wants to throw whatever is in the pantry into the blender to make a smoothie or soup. (Note that I am being unfair to people with immersion blenders. I am not being unfair to people who use VARs.)
The VAR appeared because economists became convinced that structural macroeconometric models are subject to the Lucas Critique, which says that as monetary policy attempts to manipulate demand, people will adjust their expectations. My reaction to this is
(a) the Lucas critique is a minor theoretical curiosity. There are much worse problems with macroeconometrics in practice.
(b) How the heck does running a VAR exempt you from the Lucas Critique? A VAR is no less subject to breakdown than is a structural model.
The macroeconometric project that I first worked with is doomed to fail. Implicitly, you are trying to make 1988 Q1 identical to 2006 Q3 except for the one causal factor with which you are concerned. This cannot be done. There is too much Manzian causal density.
The VAR just takes this doomed macroeconometric project and cavalierly ignores details. It is not an improvement over the macroeconometrics that I learned in the 1970s. On the contrary, it is inferior. And if the big names in modern macro all use it, that does not say that there is something right about VAR. It says that there is something wrong with all the big names in modern macro. On this point, Robert Solow and I still agree.
I use VARs to project loss severity and frequency for insurance exposure. Mostly it they me some kind of covariance to work with, and I have found that they are usually more skillful than someone just arm waving a selected value (which is what is usually done). There are exogenous factors that the models cannot possibly know about, so I try to do my best to adjust the data before the model is fit, that and, dummy variables help.
Anyway, I have no idea how broad your dislike of the method is, but I was curious if there was any instance where you thought they might be appropriate, or at least better than nothing?
I can invent a term. Vector auto entropy. Replace time with probability of occurance. Replace regression with entropy. Works pretty well and is nothing but structural.
A lot of academics have the strange idea that making regressions more complex can eliminate the problem with regressions. Some of the problems can be mitigated (some problematic assumptions, distributional issues, auto-correlation, etc) but it’s always good to remind everyone that the basic nature of the process (non-experimental data with massive quantities of unknown or latent influences) can’t be mathematized-away. Any thoughts on simulation/agent-based modeling as a substitute? Particularly where grounded in observed data.