Wednesday, August 9, 2017

Brian Romanchuk — Rigour And Macroeconomics

Much of my writing about macroeconomic theory is of the hand-wringing variety: it cannot be "scientific" because (useful) forecasting is essentially impossible to do. This is a negative (non-constructive) argument; but that does not mean that we cannot be rigorous.
As a comment on my previous article ("Science and Economics") André asked, "If we are unable to test macroeconomic theory, how will we know that it works?" If we use a wide definition of "test," we are able to do so. However, this notion of "testing" would probably raise eyebrows among physical scientists, who perhaps assume that "forecasting" and "testing" would be the same thing in this context. It is possible to look at macro in a rigorous way, but we need to drop the embedded assumption that rigorous means the same thing as acting like physicists.
My arguments here should not actually be surprising to economists, as they are effectively a hidden background assumption in their worldview. Instead, this viewpoint is aimed at non-economists who want to treat macroeconomics like other fields of knowledge.
Brian ventures into philosophy of macroeconomics, a subject that most economists avoid, which means that they presuppose the foundations of their discipline, which implies that they impose their view based on hidden assumptions. Foundational studies attempt to clarify these matters.

I think a good approach to scientific rigor comes from Richard Feynman's observation that a key purpose of science as rigorous thinking is to keep from fooling ourselves. See his Cargo Cult Science.

 Generally speaking, these days "scientific" means using a formal model to represent actual change of events over time in accordance with some invariance that allows for prediction and therefore testing hypotheses. The rigor comes from both rigorous thinking provided by format ionization and also from the ability to compare a model with reality in order to determine its degree of representation. since models are simplification, few models will be exact replicas of event. They don't need to be in order to be useful for the purpose constructed.

So the first step is to generate a model based on assumptions. At the macro level such models are usually understood to be explanatory models in the sense of modeling some mechanism or transmission process that captures some useful level of invariance in changing events.

This implies that the properties of models must reflect the actual pattern of changing events.

This enables testing the model against what is modeled through observational checking.

A fundamental assumption is that the future resembles the past in the area of consideration to be able to identify invariance. Analysis of data from observing the past is indicative but not definitive, and all models are contingent on future observations. Science is therefore tentative.

The greater the degree of ergodicity, the more representational models can be. As uncertainly increases, models necessarily become less rigorous in the sense that the assumptions map future events if the reasoning is correct.

In the case of non-ergodicity, no amount of rigor in model construction or reasoning can guarantee that the future will continue to resemble the past in the way that such models suggest.

The greatest degree of rigor is provided by deterministic functions, which necessitates the ability to measure variables. The next degree of rigor is provided by stochastic functions which allows for estimation based on sampling, for example.

Biology accounts for a degree of apparent determinism in human behavior. Custom, habit account and path dependence account for some observed degree of patterned behavior in human affairs, but this tends to be local rather than universal.

However, where radical uncertainty exists, contingent models are needed, including conceptual models that take matters into consideration that are difficult to impossible to model formally. For instance, science is presumed to be consilient, so that assumptions that conflict with other areas are suspect.

Moreover, there is also a tendency to overgeneralize, fit curves, fudge and nudge, and even see faces in clouds. For example, there is a tendency to overgeneralize by projecting oneself and one's in-group on humanity and concluding that local characteristics are universal. This is the basis of much that is assumed about "human nature."

The result has been that in the social science, including economics, a distinction has been drawn between the micro and macro levels of scope and scale. The micro has tended to assume dominance, since the scope and scale permit a greater degree of rigor that is confirmed at least statistically.

Grand theories that explain behavior at the societal level have fallen out of favor because they are difficult to construct formally and also difficult to measure observationally. So the usefulness of such theories questionable and they fall victim to the charge of being speculative rather than scientific.

Grace O. Okafor's "Grand Theories and Their Critiques: From C. Wright Mills to Post Modernism" explores this in the history of sociology. It is not difficult to find parallels in the history of economics. 

Gary Becker's rational choice approach has spread from economics to the other social sciences as a framework for modeling social, political and economic behavior. This has led to criticism from several angles — bounded rationality, cognitive-affective bias, different types of decision making, contextual asymmetries, reflexivity and emergence, and uncertainty, for example.

Bond Economics
Rigour And Macroeconomics
Brian Romanchuk


AXEC / E.K-H said...

Economists: only good at excuses
Comment on Brian Romanchuk on ‘Rigour and Macroeconomics’

Brian Romanchuk introduces himself: “Much of my writing about macroeconomic theory is of the hand-wringing variety: it cannot be ‘scientific’ because (useful) forecasting is essentially impossible to do.”

This one sentence is enough for scientific self-debunking. Economics is a cargo cult science because economists never understood what science is all about. Proof No 1: like the average commonsenser, economists maintain erroneously that science is about predicting the future.

John Kay, for example, explains why this does not work in economics: “Big data can help us understand the past and the present but it can help us understand the future only to the extent that the future is, in some relevant way, contained in the present. That requires a constancy of underlying structure that is true of some physical processes but can never be true of a world … in which important decisions or discoveries are made by processes that are inherently unpredictable and not susceptible to quantitative description.”

This so trivial that it hurts and, above all, it is entirely beside the point. It is not a specific failure of economics that it cannot predict the future because — as a matter of principle — science is NOT AT ALL in the business of prediction because it is long known among scientists: “The future is unpredictable.” (Feynman)#1 Only charlatans predict the future, and only morons take them seriously.

The first thing to understand is that science is NOT about prediction but about knowledge. So, to begin with, things that are not knowable are a priori OUT of science. Scientific knowledge satisfies two criteria: material and formal consistency. Everything else is storytelling, sitcom blather, clueless filibuster, and hand waving.

Scientific knowledge is embodied in the true theory. The true theory is the best possible mental representation of reality.

Economists do not have the true theory and the representative economist does not realize that the four main approaches ― Walrasianism, Keynesianism, Marxianism, Austrianism ― are mutually contradictory, axiomatically false, materially/formally inconsistent and that ALL got the pivotal economic concept profit wrong.

The representative economist is content with the pluralism of provably false theories and he simply tries to explain/excuse manifest failure away.#2 The recurring key words are complexity, non-ergodicity, radical uncertainty, emergence, novelty, spontaneity of human behavior, and so on and on.

Economists do not understand that their subject matter is ill-defined. Economics is NOT a social science but a system science. The lethal methodological defect of economics is that it is microfounded, that is, based on behavioral axioms. Now it holds that (1) there is NO such thing as an invariant of human behavior, and (2), NO way leads from the explanation of Human Nature/motives/behavior/action to the explanation of how the economic system works.

See part 2

AXEC / E.K-H said...

Part 2

Economics is NOT AT ALL about Human Nature/motives/behavior/action. This is the subject matter of psychology, sociology, anthropology, history, political science, biology, etc. Economics is about the economic system and objective systemic laws.#3

The ultimate proof of utter scientific incompetence is that neither orthodox nor heterodox economists have gotten the foundational concepts of their subject matter ― profit and income ― right. This is embarrassing, laughable, and inexcusable.

Economics has to be macrofounded and this requires the full replacement of false Walrasian microfoundations and false Keynesian macrofoundations.#4 Economics is not a science until this day because economists are nothing but cargo cultic blatherer.#5, #6

Egmont Kakarot-Handtke

#1 Scientists do not predict

#2 Failed economics: The losers’ long list of lame excuses

#3 First Lecture in New Economic Thinking

#4 Macro for dummies

#5 Economists: scientists or political clowns?

#6 With regard to MMT, in particular, see cross-references