tag:blogger.com,1999:blog-17232051.post1603649980633101054..comments2024-03-18T22:32:52.802-04:00Comments on Noahpinion: Big Ideas in Macroeconomics book review, Part 2: Science, math, and macroNoah Smithhttp://www.blogger.com/profile/09093917601641588575noreply@blogger.comBlogger42125tag:blogger.com,1999:blog-17232051.post-45590478848296752012014-06-19T05:56:53.872-04:002014-06-19T05:56:53.872-04:00That's such a great example of the pointlessne...That's such a great example of the pointlessness of attaching mathematical magnitudes to unquantifiable, incommensurable properties.Unlearningeconhttps://www.blogger.com/profile/13687413107325575532noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-87777490761858745312014-06-19T05:55:45.971-04:002014-06-19T05:55:45.971-04:00Krzys, geologists may not be able to predict the e...Krzys, geologists may not be able to predict the exact timing of earthquakes but they do know (roughly) where they are most likely to occur, at what frequency and magnitude. They understand why and how they happen. This means that people can at least prepare for them or be aware that they're a possibility in a given area. <br /><br />I don't see how economists have achieved anything comparable in macro.Unlearningeconhttps://www.blogger.com/profile/13687413107325575532noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-14855736036083451442014-06-19T05:53:18.057-04:002014-06-19T05:53:18.057-04:00It's very well using maths to try and be preci...It's very well using maths to try and be precise and clear about your ideas, but if the properties in the equations are not directly observable (utility, TFP) then I don't see how your ideas are actually linked to the real world. In other words, you're being precise about absolutely nothing.Unlearningeconhttps://www.blogger.com/profile/13687413107325575532noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-59332870605332376142014-06-18T14:15:12.105-04:002014-06-18T14:15:12.105-04:00No, I am saying both geology and econ are sciences...No, I am saying both geology and econ are sciences.Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-14174931595176637522014-06-18T12:11:14.252-04:002014-06-18T12:11:14.252-04:00So you are saying geology isn't a science?So you are saying geology isn't a science?A Hhttps://www.blogger.com/profile/06916657901677009228noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-47733746575335070732014-06-18T03:01:52.199-04:002014-06-18T03:01:52.199-04:00What would a macro comment thread be without "...What would a macro comment thread be without "complex systems" mysticism? Nowhere, that's where.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-17232051.post-24234838734772606372014-06-18T01:56:35.442-04:002014-06-18T01:56:35.442-04:00The medicine example is almost too perfect here. Y...The medicine example is almost too perfect here. You should acquaint yourself with works of John Ioannidis.Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-18587889054973662642014-06-18T01:53:26.680-04:002014-06-18T01:53:26.680-04:00@Jason
the earthquake example shows you that even ...@Jason<br />the earthquake example shows you that even if you know the underlying dynamics perfectly (which we don't for econ), we still can't understand the aggregate behavior. Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-54349233703247343072014-06-17T20:19:25.118-04:002014-06-17T20:19:25.118-04:00@Ryan,
Thank you. John Haltiwanger definitely get...@Ryan,<br /><br /><em>Thank you</em>. John Haltiwanger definitely gets it. The "manifesto" you linked should be required reading in every econometrics course [1]:<br /><br /><em>Here is the vision. A social scientist or policy analyst [...] is investigating the impact of the “great” recession and anemic recovery (as of September 2010) on businesses and workers. The analyst begins by exploring the latest aggregate data showing economy-wide, sector-level and broad regional-level variation in terms of business productivity, output, capital investment, prices, wages, employment, unemployment and population. The data on employment changes can be decomposed into hiring, quits, layoffs, job postings, job creation and destruction. The data on unemployment can be decomposed into gross worker flows tracking flows into and out of unemployment. The data on workers is linked to measures from household data tracking income, consumption, wealth, consumer finances and household composition. The data are high frequency (monthly or quarterly) and timely (data for the most recent quarter or month). The data are available not only for the present time period but historically for several decades permitting analysis of both secular trends and cyclical variation.<br /><br />[...] The analyst can conduct empirical studies at the economy-wide, broad sectoral and broad regional level with data broken down by all of these dimensions. In addition, the analyst can drill down to the individual and firm level creating a longitudinal matched employer-employee data set with all of this information at the micro level. This permits panel data analysis using rich cross sectional and time variation data tracking the outcomes of businesses, workers and households. These outcomes can be tracked at the very detailed location (Census block or track) and detailed characteristics level. The drilled down data aggregates to the national key indicators that receive so much attention.<br /><br />The analyst can ascertain, for example, is it really the case that it is small, young businesses that normally would be creating jobs given their productivity and profitability who can’t get credit that is accounting for the anemic recovery as of September 2010. The analyst could track what<br />type of financing has especially decreased relative to other economic recoveries. The analyst could analyze the impact of policy interventions historically and how they have or have not had influence on different types of businesses and in turn on the workers employed by these businesses.<br /></em><br /><br />[1] http://www.aeaweb.org/econwhitepapers/white_papers/John_Haltiwanger.pdfAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-17232051.post-2998539362638324832014-06-17T19:21:32.485-04:002014-06-17T19:21:32.485-04:00In science there is the concept of "significa...In science there is the concept of "significant digits". Don't report a result to the nearest hundredth if your ruler only measures to 64ths. Economics uses math and in doing so ignores the reality that it should report results with zero significant digits. <br /><br />The debate here will be used by future philosophers of science as a case study: this is what it looks like inside the old paradigm, right before the shift. The poor creatures have no idea. <br /><br />Meanwhile, complex systems analysis correctly predicted the path of Superstorm Sandy. But that's an entire field working under a productive model. The tiny minority in Econ doing the same are just now picking up steam. Thornton Hallhttps://www.blogger.com/profile/11402495641975262697noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-73025049650234886612014-06-17T16:14:31.174-04:002014-06-17T16:14:31.174-04:00@Jason But, with better data, we would have better...@Jason But, with better data, we would have better foundations. All foundations come from data; otherwise we are just human brains clouded in darkness and silence. Astronomers can do well without lots of data because they do actually have lots of data on similar phenomena. If macro had more data, then, like physicists, economists could develop good macro models based on consistent foundations.Joshua Weisshttps://www.blogger.com/profile/10313541087554190148noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-23742186490836998652014-06-17T16:09:45.915-04:002014-06-17T16:09:45.915-04:00Anonymous - You are definitely right about the bia...Anonymous - You are definitely right about the bias. My point is that, whether you use math or not, the bias is there. If your literary model can give you useful results (beyond idea generation that is only indirectly useful), then you should be able to write it down mathematically and solve it. If you can't, then I'm skeptical that your logic is sound. It is almost always more difficult to spot logical flaws or assumptions when math is not used. Of course, this does not mean that a non-math model is bad. It just means that, when confronted with it, I'll say "let's try to write it mathematically and see if it still makes sense."Joshua Weisshttps://www.blogger.com/profile/10313541087554190148noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-12260698743773160392014-06-17T13:44:02.338-04:002014-06-17T13:44:02.338-04:00As someone whose economics degree is more than fou...As someone whose economics degree is more than four decades old (but who earned a living from the subject for most of my career), I've found it increasingly difficult to keep up with what counts as "progress" in the discipline. It seems to me that the parts that are mathematical are not very useful, and the parts that are useful are not very mathematical. Jimhttps://www.blogger.com/profile/07110218804343804328noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-68215662128460889402014-06-17T12:07:41.780-04:002014-06-17T12:07:41.780-04:00That's irrelevant. People are things, too.That's irrelevant. People are things, too.Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-20287793433745683592014-06-17T12:06:12.243-04:002014-06-17T12:06:12.243-04:00Again, how well are the earthquake predictions goi...Again, how well are the earthquake predictions going?Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-6169576910217780632014-06-17T09:13:43.009-04:002014-06-17T09:13:43.009-04:00Ryan Decker- Fair point but I would point out that...Ryan Decker- Fair point but I would point out that a lot of the building blocks of these models are based around "tractable" elements. Furthermore, this trend is rather late in the day- economics is based on 70+ years of analytical solutions which informs the foundations of the discipline and ignores the "hard stuff". Analytic solutions, incidentally, still rule the world in micro and I suspect that there is still quite a lot in macro.<br /><br />Joshua Weiss- I'm not sure of your point here. My point is that there is a systematic bias in favour of *analytical* solutions to models and any models that cannot be *analytically* solved tend to get thrown out (or at least given lower weight).<br /> A tractable model may give you nice, precise predictions but if they are wrong then they are pretty useless. Being vaguely right is better than being precisely wrong. Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-17232051.post-45760954068906511922014-06-17T00:51:16.303-04:002014-06-17T00:51:16.303-04:00Ok but what about other relatively data-scarce, mo...Ok but what about other relatively data-scarce, mostly observational sciences like Medicine or Paleontology that <em>also</em> cannot straightforwardly derive their core theories directly from well-established experimental sciences?<br /><br />Like Jason points out, this trope of constantly blaming the appalling state of modern macro on unproven "complexity spirits" smacks of begging the question and is getting old. <em>Of course</em> science is hard: we would have figured it out millenia ago otherwise. An altogether different matter though is whether a given phenomenon, like earthquake fractures or the three-body problem, is <em>provably</em> hard to measure/predict, even approximately. And macro is remarkably silent on this.<br /><br />To be fair, macro does have some hardness theorems: the Lucas critique, for one; or the most recent work on the NP-completeness of market clearing. But on concrete questions of statistical power and information-theoretical model inference, the discipline falls remarkably short. Cosma Shalizi tried unsuccessfully for some time to convince macroeconomists to formalize and quantify what they mean by "we don't have enough data for our complicated models", to no avail so far.<br /><br />But hey: why change the status quo if once you're in all it takes to be a "macroeconomist" today is play around on the blackboard with some half-baked frictions, log-linearize around the steady state, and match some dubious moments or, if feeling adventurous, "confirm" your empirical win by some poorly-reasoned IV on 60 data points over the weekend... Plus you get to wear a tie unironically (since, chances are, you'll be male) and be quoted on the WSJ, etc.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-17232051.post-43015208733916186912014-06-17T00:41:09.625-04:002014-06-17T00:41:09.625-04:00Excusing macro as "hard" is like excusin...Excusing macro as "hard" is like excusing the hubris of Marxists and their dialectic systems because modeling future history is hard. Suppose Freud had expressed the Oedipus complex as<br /><br />Oe = Dm - Fc<br /><br />where<br />Oe = degree of Oedipal predilection<br />Dm = the son's sexual desire for his mother<br />Fc = fear of castration by his father<br /><br />Does the use of mathematical language, by itself, legitimize unfalsifiable theories and tautologies by giving them "precision of meaning"? How much weight should we give to equations made of unmeasurable free parameters like utils, propensities, and expectations? <br /><br />Anonymoushttps://www.blogger.com/profile/02977684524676621423noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-72818170314050914782014-06-16T19:19:14.712-04:002014-06-16T19:19:14.712-04:00"There is a second, even more substantial dif..."There is a second, even more substantial difference from the physical sciences: for most important macroeconomic questions, macroeconomists cannot conduct controlled experiments."<br /><br />Historical geology has this constraint, and manages to be pretty science-y.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-17232051.post-77293662888603336212014-06-16T19:07:04.376-04:002014-06-16T19:07:04.376-04:00@Joshua Yes, exactly. If your assumptions lack fir...@Joshua Yes, exactly. If your assumptions lack firm grounding, then it doesn't matter how much or how little data you have.<br /><br />@Krzys Earthquake prediction is in a better situation since the underlying physics assumptions are reasonably well grounded. We can say it is a complex or unpredictable system with meaning (e.g. materials failure can be unpredictable in the lab). We can't really call an economic system complex or unpredictable because we don't have the underlying assumptions on which to base that claim -- it's complexity and unpredictability would have to be assumed from our ignorance. Another way, earthquake prediction is provably hard. We can't prove economics is unpredictable yet.Jason Smithhttps://www.blogger.com/profile/12680061127040420047noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-40517149797915624532014-06-16T16:23:07.117-04:002014-06-16T16:23:07.117-04:00If a model is too hard to solve, then it is probab...If a model is too hard to solve, then it is probably unclear what will happen to various variables of interest after a shock. For example, suppose my agents have some complicated utility functions, so, after the labor tax is increased, it is difficult to determine whether aggregate labor supply goes up or down. Or, maybe it is difficult to determine whether average productivity will go up or down. In that case, there are multiple forces at work, and I can predict whatever I want without solving my model; I can say "this force will drive down productivity when you increase taxes" or I can say "this force will drive up productivity when you increase taxes". My model (literary or mathematical) is untestable and not useful.<br /><br />Noah's right that non-mathematical models can be useful for exploring ideas. But, ultimately, you can't get much policy advice or concrete predictions without a solvable mathematical model (solvable analytically or through calibration and simulation).Joshua Weisshttps://www.blogger.com/profile/10313541087554190148noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-13993093007473815882014-06-16T16:14:44.208-04:002014-06-16T16:14:44.208-04:00Astronomy does not really have as limited data as ...Astronomy does not really have as limited data as economics. We have a good understanding of the fundamental laws of physics, which we can apply to astronomy. For example, the laws of thermodynamics apply in outer space just as much as they do on Earth. In other words, the "assumptions" in astronomy are derived from physics, which has plenty of data.<br /><br />On the other hand, we do not have a good understanding of the fundamental laws of preferences and choice. If we did, then the only difficulty in macro would be aggregation.Joshua Weisshttps://www.blogger.com/profile/10313541087554190148noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-83839013863378271132014-06-16T14:53:22.801-04:002014-06-16T14:53:22.801-04:00Having conceptual clarity is invaluable, but math ...Having conceptual clarity is invaluable, but math is extremely useful in helping you ask better questions of data. philosophical fields often degenerate into empty arguments over what the prophet really meant. A particularly pathetic waste of timeKrzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-59591509771912576922014-06-16T14:49:54.750-04:002014-06-16T14:49:54.750-04:00data sets much bigger and the system dynamics much...data sets much bigger and the system dynamics much simpler. To see how vexing complexity is try predicting earthquakes.Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.comtag:blogger.com,1999:blog-17232051.post-54458398117254177362014-06-16T14:46:48.725-04:002014-06-16T14:46:48.725-04:00Complexity is your enemy when dealing with limited...Complexity is your enemy when dealing with limited data sets. Trying to make more "realistic" (meaning complex) models is a way to nowhere: tractable and simple models are the only way.Krzyshttps://www.blogger.com/profile/15794655390770135247noreply@blogger.com