tag:blogger.com,1999:blog-6837159629100463303.post8723397533326252214..comments2023-06-18T01:25:08.748-07:00Comments on Information Transfer Economics: Barriers to entry in the quantitative parable industryJason Smithhttp://www.blogger.com/profile/12680061127040420047noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-6837159629100463303.post-89220986698493391472017-06-22T09:59:04.817-07:002017-06-22T09:59:04.817-07:00Yep, that's how I read it.Yep, that's how I read it.Tom Brownhttps://www.blogger.com/profile/17654184190478330946noreply@blogger.comtag:blogger.com,1999:blog-6837159629100463303.post-68734037214908799552017-06-22T09:09:07.296-07:002017-06-22T09:09:07.296-07:00Thanks, Tom.Thanks, Tom.Jason Smithhttps://www.blogger.com/profile/12680061127040420047noreply@blogger.comtag:blogger.com,1999:blog-6837159629100463303.post-43927416378740339932017-06-22T09:08:47.623-07:002017-06-22T09:08:47.623-07:00Hello Coker,
Re: straw man
I think you have misu...Hello Coker,<br /><br />Re: straw man<br /><br />I think you have misunderstood my post. Cochrane is effectively making the same point I am making in your quote (that is basically the point of my diagram and steps 1-4 in the recipe), however the main point of my post (it being referenced in the title and is the first item I discuss) is that Cochrane *in addition* believes "quantitative parables" can come before any empirically successful models or frameworks.<br /><br />I in no way say you shouldn't try to model a phenomenon before you have a "single, successful, highly complex model" (i.e, a framework). I am saying that the pre-framework modeling should always be compared to empirical data.<br /><br />"Quantitative parables" that don't reference data or that theoretically isolate mechanisms should come only after you have a framework.<br /><br />The question you should have in your head is "How can you possible isolate a mechanism using theory when the theory you have has limited scope (i.e. hasn't been consolidated into a broadly successful framework)?"<br /><br />Take minimum wage studies for example. How can I possibly know by theoretical means that my empirical study design takes into account all possible effects when I don't have theoretical means that broadly explains wages in general (i.e. a framework for the labor market)?<br /><br />Currently, this is accomplished via handwaving about instrumental variables and natural experiments which are fine. Those are empirical studies.<br /><br />But I cannot propose a mechanism *via theory* (a "quantitative parable") that isolates the effects of minimum wages because no broadly accurate model of wages exists. If the model explains the data well, then that is sufficient justification. But if it doesn't, then you really haven't accomplished anything.<br /><br />Now you can try to come up with any model for wage data, but the test of that is empirical data. If the model gets the data broadly right, then that's good! But if the model gets the data wrong then it is useless.<br /><br />Re: Keynes<br /><br />I was quoting Keynes as a counterexample of good mathematical modeling. Keynes didn't think you should "fill in real values", which basically means Keynes is talking about mathematical philosophy which anyone with a basic education (i.e. not just economics PhDs) can participate in.Jason Smithhttps://www.blogger.com/profile/12680061127040420047noreply@blogger.comtag:blogger.com,1999:blog-6837159629100463303.post-57638624700854927952017-06-22T03:45:48.581-07:002017-06-22T03:45:48.581-07:00Leaving aside the doubtful logic of citing Keynes ...Leaving aside the doubtful logic of citing Keynes on quantitative economic modelling, you're attacking a straw man. Cochrane himself notes (which you don't mention):<br />"Economics remains quite different from physics in that way. The underlying ingredients of (say) a climate or aircraft design model are very well understood, so you can make complex models that work. The underlying ingredients of economic models are not so well understood -- how much more will people work if their wages rise, how do they interpret statements by government officials, how do companies change their prices, and so on -- that small changes in the little ingredients make big differences in the economy-wide outcomes."<br /><br />Simply because one can't (yet) formulate a single, successful, highly complex model of a phenomenon doesn't mean you shouldn't try to model that phenomenon at all.Cokerhttps://www.blogger.com/profile/02631288439451432057noreply@blogger.comtag:blogger.com,1999:blog-6837159629100463303.post-62008418668833683242017-06-21T10:36:06.004-07:002017-06-21T10:36:06.004-07:00Interesting post!
Interesting post!<br />Tom Brownhttps://www.blogger.com/profile/17654184190478330946noreply@blogger.com