Economic models are getting more and more sophisticated — and totally useless18 January, 2017 at 18:27 | Posted in Economics | Leave a comment
Those of us in the economics community who are impolite enough to dare question the preferred methods and models applied in mainstream economics, are as a rule met with disapproval. But although people seem to get very agitated and upset by the critique — just read the commentaries on this blog if you don’t believe me — defenders of “received theory” always say that the critique is “nothing new”, that they have always been “well aware” of the problems, and so on, and so on.
So, for the benefit of all mindless practitioners of mainstream economic modeling — who defend mainstream economics with arguments like “the speed with which macro has put finance at the center of its theories of the business cycle has been nothing less than stunning,” and re the patently ridiculous representative-agent modeling, maintain that there “have been efforts to put heterogeneity into big DSGE-type models” but that these models “didn’t get quite as far, because this kind of thing is very technically difficult to model,” and as for rational expectations admit that “so far, macroeconomists are still very timid about abandoning this pillar of the Lucas/Prescott Revolution,” but that “there’s no clear alternative” — and who don’t want to be disturbed in their doings, eminent mathematical statistician David Freedman has put together a very practical list of vacuous responses to criticism that can be freely used to save your peace of mind:
We know all that. Nothing is perfect … The assumptions are reasonable. The assumptions don’t matter. The assumptios are conservative. You can’t prove the assumptions are wrong. The biases will cancel. We can model the biases. We’re only doing what evereybody else does. Now we use more sophisticated techniques. If we don’t do it, someone else will. What would you do? The decision-maker has to be better off with us than without us … The models aren’t totally useless. You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Where’s the harm?