March 24, 2023

Econometric FUQs

Running paper and pen experiments on synthetic analogue model economies is a sure method of establishing (causal) economic laws or fixing detailed econometric problems of autonomy, identification, invariance and structural stability– in the design world. Instead traditional economic experts always have to load their theories and models with sets of auxiliary structural presumptions to get any outcomes at all in their designs.
Some financial methodologists have actually recently been arguing that financial designs might well be thought about very little designs that depict reputable worlds without having to care about things like similarity, isomorphism, simplified representationality or similarity to the real world. Building models that intend for explanatory insights might not enhance the models for making (quantitative) forecasts or provide some kind of understanding of whats going on in the intended target system. For one purpose in one context design A is finest, for other purposes and contexts design B might be deemed finest.

from Lars Syll
If you cant develop an experiment that answers your concern in a world where anything goes, then the chances of creating helpful results with a modest spending plan and nonexperimental study information seem quite slim. The description of a perfect experiment likewise assists you create causal concerns exactly. The mechanics of an ideal experiment highlight the forces you d like to control and the aspects you d like to hold consistent.
Research study questions that can not be responded to by any experiment are FUQs: basically unknown questions.
One of the constraints of economics is the limited possibility to carry out experiments, forcing it to mainly rely on observational studies for knowledge of real-world economies.
Still– the concept of carrying out lab experiments holds a firm grip on our dream to discover (causal) relationships between economic variables. If we just could separate and manipulate variables in regulated environments, we would probably discover ourselves in a circumstance where we with higher rigour and accuracy might explain, predict, or describe economic happenings in terms of structural causes, specification values of appropriate variables, and economic laws..
Galileo Galileis experiments are frequently held as exemplary for how to perform experiments to discover something about the genuine world. Galileos heavy balls dropping from the tower of Pisa, verified that the range an item falls is proportional to the square of time and that this law (empirical regularity) of falling bodies could be relevant outside a vacuum tube when e. g. air presence is negligible.
The big issue is to discover or choose out exactly for which objects air resistance (and other possibly puzzling aspects) is minimal. When it comes to heavy balls, air resistance is certainly negligible, but how about plumes or plastic bags?
One possibility is to take the all-encompassing-theory road and find out all about possible disturbing/confounding aspects– not just air resistance– influencing the fall and develop that into one terrific model providing accurate forecasts on what happens when the object that falls is not just a heavy ball but plumes and plastic bags. This usually totals up to eventually stating some kind of ceteris paribus analysis of the law..
Another road to take would be to focus on the negligibility presumption and to define the domain of applicability to be just heavy compact bodies. The price you have to pay for this is that (1) negligibility may be tough to develop in open real-world systems, (2) the generalization you can make from sample to population is heavily restricted, and (3) you actually have to use some shoe leather and empirically look for out how big is the reach of the law..
In traditional economics, one has normally gone for the theoretical road (and in case you think today natural experiments hype has altered anything, keep in mind that to mimic real experiments, exceptionally strict unique conditions standardly have to obtain).
In the end, it all boils down to one concern– are there any Galilean heavy balls to be found in economics, so that we can indisputably develop the presence of economic laws operating in real-world economies?
As far as I can see there are some heavy balls out there, but not even one single genuine economic law.
Economic factors/variables are more like plumes than heavy balls– non-negligible aspects (like air resistance and chaotic turbulence) are hard to eliminate as having no impact on the item studied.
Galilean experiments are tough to carry out in economics, and the theoretical analogue designs economists construct and in which they perform their believed experiments build on assumptions that are far from the kind of idealized conditions under which Galileo performed his experiments. The nomological devices that Galileo and other scientists have been able to construct have no real analogues in economics. The stability, autonomy, modularity, and interventional invariance, that we may find between entities in nature, just are not there in real-world economies. Thats are real-world reality, and contrary to the beliefs of the majority of mainstream economic experts, they will not disappear simply by using deductive-axiomatic economic theory with lots of basically dubious assumptions.
By this, I do not mean to state that we need to discard all (causal) theories/laws developing on modularity, stability, invariance, etc. But we need to acknowledge the truth that outside the systems that perhaps fulfil these requirements/assumptions, they are of little substantial worth. Running paper and pen experiments on synthetic analogue model economies is a sure way of establishing (causal) economic laws or fixing elaborate econometric issues of autonomy, identification, invariance and structural stability– in the design world. They are pure replacements for the genuine thing and they dont have much bearing on what goes on in real-world open social systems. Establishing practical circumstances for carrying out Galilean experiments may tell us a lot about what happens under those type of scenarios. However– few, if any, real-world social systems are hassle-free. So the majority of those systems, theories and designs, are irrelevant for letting us know what we really wish to know.
To solve, understand, or explain real-world issues you actually have to understand something about them– logic, pure mathematics, information simulations or deductive axiomatics do not take you extremely far. Many econometrics and economic theories/models are superb reasoning machines. — using them to the genuine world is a totally hopeless endeavor! The assumptions one needs to make in order to effectively use these deductive-axiomatic theories/models/machines are devastatingly restrictive and mainly empirically untestable– and hence make their real-world scope extremely narrow. To fruitfully evaluate real-world phenomena with theories and models you can not build on patently and known to be extremely unreasonable assumptions. No matter just how much you would like the world to totally include heavy balls, the world is not like that. The world likewise has its reasonable share of plumes and plastic bags.
Many of the idealizations we discover in mainstream economic designs are not core assumptions, but rather structural auxiliary presumptions. Without those extra presumptions, the core presumptions provide next to absolutely nothing of interest. To come up with interesting conclusions you have to rely greatly on those other– structural– presumptions.
In physics, we have theories and centuries of experience and experiments that reveal how gravity makes bodies move. In economics, we understand there is absolutely nothing comparable. Rather mainstream economists always have to load their theories and models with sets of auxiliary structural assumptions to get any outcomes at all in their designs.
So why then do traditional financial experts keep on pursuing this modelling task?
Mainstream as if models are based on the logic of idealization and a set of tight axiomatic and structural assumptions from which exact and constant reasonings are made. The beauty of this procedure is, naturally, that if the assumptions hold true, the conclusions always follow. But it is a bad guide for real-world systems.
The way axioms and theorems are developed in mainstream economics frequently leaves their spec without nearly any constraints whatsoever, safely making every you can possibly imagine proof compatible with the expansive theory– and theory without informative material never ever runs the risk of being empirically tested and discovered falsified. Utilized in mainstream thought speculative activities, it might, of course, be extremely convenient, however absolutely space of any empirical value.
Some financial methodologists have lately been arguing that financial designs may well be considered minimal designs that portray credible worlds without having to care about things like similarity, isomorphism, streamlined representationality or resemblance to the real world. These designs are said to resemble practical novels that represent possible worlds. And sure: economic experts working and constructing with those type of designs find out things about what might take place in those possible worlds. But is that really the stuff real science is made from? I think not. As long as one does not create reputable export warrants to real-world target systems and show how those designs– frequently developing on idealizations with understood to be false assumptions– improve our understanding or descriptions about the real life, well, they are simply nothing more than simply books. Showing that something is possible in a possible world does not offer us a justified license to presume that it therefore likewise is possible in the genuine world. The Great Gatsby is a fantastic book, however if you truly wish to discover what is going on the planet of financing, I would advise rather checking out Minsky or Keynes and straight confronting real-world finance.
Constructing designs that intend for explanatory insights may not optimize the designs for making (quantitative) predictions or provide some kind of understanding of whats going on in the desired target system. For one function in one context design A is finest, for other functions and contexts design B might be considered finest. Even so, I would argue that if we are looking for what I have called adequate descriptions (Syll, Ekonomisk teori och metod, Studentlitteratur, 2005) it is not adequate to simply come up with minimal or reputable world models.
The presumptions and descriptions we utilize in our modelling have to be true– or at least harmlessly incorrect– and offer a sufficiently comprehensive characterization of the mechanisms and forces at work. Designs in traditional economics not do anything of the kind.
Our aspirations have to be more significant than simply constructing meaningful and reputable designs about possible worlds. You have to supply definitive empirical evidence that what you can infer in your model also assists us to uncover what actually goes on in the real world. Without giving that kind of information it is impossible for us to check if the possible world designs you come up with really hold likewise for the one world in which we live– the genuine world.
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