Bodo podatki izrinili teorijo?

Ena izmed ključnih zamer v makroekonomiji zadnjih 40 let je, da so teoretski modeli z divjimi predpostavkami (kot so racionalna pričakovanja ali hipoteza permanentnega dohodka) prevladali. Ti povsem teoretski modeli so posilili makroekonomijo z hudo restriktivnim optimizacijskim obnašanjem “ekonomskih agentov”. In tudi kadar je makroekonomija poskšala biti aplikativna z empiričnimi modeli (prevedba neoklasičnega modela v Lucasovi tradiciji v neokeynesiansko generacijo makro DSGE modelov), so bile te predpostavke tako restriktivne, da so se ti empirični modeli pokazali kot povsem neuporabni za napovedovanje česarkoli (o tem sem večkrat pisal).

No, Noah Smith je v zadnji kolumni malce hitro in najbrž naivno zmagoslavno zapisal, da je z dostopnostjo in uporabo podatkov (predvsem ekperimentalne narave) prišlo do izrivanja te restrktivne teorije s pragmatično uporabo podatkov. Torej: za razumevanje nekega pojava ne bo več treba razmišljati o tem, kako prisiljeno optimizacijsko se morajo v dani situaciji obnašati gospodinjstva in podjetja, pač pa preprosto z nekim ekonometričnim modelom ocenite, kakšen je vpliv x na y. Torej ne potrebujete več “globokega razumevanja” nekega pojava, pač pa pogledate, kaj vam pokažejo podatki. Do neke mere se je s tem mogoče strinjati, vendar pa to pomeni, da bodo potrebne številne ad hoc teorije, ko se bodo podatki iz nekih novih eksperimentov in povezave med nekimi pojavi spremenili.

Po mojem mnenju je in bo še vedno potreben nek strukturiran pogled na ekonomske pojave (torej teorija oziroma razumevanje mehanizmov v ozadju nekega pojava), pri tem pa je treba zgolj sprostiti omejujoče prisilne predpostavke, kako naj bi se subjekti prisiljeno racionalno (optimizacijsko) obnašali. Ali drugače rečeno, teorijo je treba iz Lucasovega prisilnega jopiča približati nazaj realnosti. Če je Lucasova šola teoretskega pristopa k ekonomiji napačna, to še ne pomeni, da je treba teorijo kot takšno zanemariti, pač pa, da je samo treba Lucasove mrtve rokave teorije zamenjati z bolj realističnimi koncepti, temelječimi na empirično dognanih spoznanjih.

To je tudi argument proti navdušencem nad “big data“, ki mislijo, da bo z dostopnostjo do (desetin) gigabajtov podatkov in mrežo računalnikov izjemne procesorske moči, ki lahko identificirajo nek vzorec obnašanja v podatkih, mogoče bolje razumeti karkoli pač in da za razumevanje tega, kaj se v podatkih dogaja, ni potrebno dobro razumevanje mehanizmov, ki oblikujejo obnašanje temeljnih gradnikov (subjektov). Buljenje v vesolje z gromozanskim teleskopom in iskanje vzorcev v svetlobnih vzorcih vam čisto nič ne pomaga pri prepoznavanju signalov v hrupu, če nimate zelo dobrega znanja fizike in globokega teoretskega razumevanja možnih mehanizmov obnašanja temeljnih delcev na elementarni ravni.

The key was the explosion of affordable information technology that made it easier to gather and analyze data. By the ’90s, there was such a huge stock of untested theories and such a wealth of new data that it made more sense for young, smart economists to turn their efforts in empirical directions. Unlike in physics, where theory and experiment call for very different skill sets, most economists found they could switch from theory to data relatively easily. Prizes like the prestigious John Bates Clark Medal awarded to rising economics stars under age 40 started to flow to people whose work emphasized data and practical applications.

But there’s a second shift in progress — a sort of Stage 2 of the data revolution in economics. The tools of empirical economists are changing. And that may cause a change in the kinds of theories that economists use as well.

The core of economics theory, as it’s practiced today, is based on individual optimization. For example, economists often assume that businesses maximize profits or minimize costs. This is known as a structural model, because economists usually assume that this sort of optimization represents the deep, fundamental structure of the economy, just like everything in your body is made up of atoms and molecules. Comparing this kind of model to data is called structural estimation, and for a while it formed the core of empirical economics.

But structural estimation has its limitations. Since structural models are usually very complicated, the answers they give to simple questions — for example, “How many people will lose their jobs if we raise the minimum wage?” — can be very sensitive to the assumptions of the model. Tweak one assumption, and the answer might come out completely wrong.

So in recent years, many economists have been turning to an alternative approach and chucking theory out the window entirely. Instead of a complicated model about optimization and utility functions and blah blah blah, just look for a case where some kind of random change in the economy — a so-called natural experiment — offers a window into some important question. For example, you could study a random influx of refugees to answer the question of how immigration affects local labor markets. You don’t need a complicated theory of how workers and companies behave — all you need is a simple linear model of how X affects Y.

The chief evangelists of this approach are economists Joshua Angrist and Jörn-Steffen Pischke. They have called the advent of natural experiments — also called quasi-experimental methods — the “credibility revolution.” And their book about the subject is titled “Mostly Harmless Econometrics.” The implication is that quasi-experimental studies, because they are more humble than structural models, are also less likely to give us the wrong answers to our most important questions.

Of course, this type of empirical economics has major limitations. Because it doesn’t give you an underlying theory about how the economy works, its predictive power diminishes rapidly as conditions change. What they gain in reliability, these studies lose in generality.

But if quasi-experimental methods continue to gain currency, they will have many economists asking why they bother with ambitious structural theories in the first place. Perhaps econ’s famous obsession with theory will become less all-consuming in the decades to come.

Vir: Noah Smith, BloombergView

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