Economistics’ humour

“All models are wrong; some are useful.” – George E. P. Box

  Приложение к Они тупы-ы-ы-е-е-е! ©

Over the years, I have learned a few things about models. I offer the following observations for your consideration:

  • The Illusion of precision: Models are imprecise. Whenever I see a forecast written out to two decimal places, I always think of the old joke: Economists like to use decimals to the hundredths to prove they have a sense of humor. But it also makes me wonder if there is a misunderstanding of the limitations of the data.

  • Direction and magnitude matter more: Forget the exact numbers, and instead concentrate on these two elements. When reviewing the output of any model, look at it over time. Does it get the general direction correct? Are the magnitude measures more or less in line with what we see in the real world? Looked at this way, the nonfarm payroll reports are much better indicators for the job market than people give them credit for.

  • Models are of limited utility: Here we get to the core of the problem: They can only do so much. We run into issues when we think they are going to solve an especially complex problem. I have heard from former Federal Reserve analysts that if the central bank can’t model something, then it doesn’t exist. I shudder to think how absurd that viewpoint is and its implications for policy.

  • Context can be problematic: This is more troubling than it looks. What I mean by this is that forcing everything into an intellectual or ideological framework may create further errors. Once everything is viewed through an imperfect lens -- and they are all imperfect -- the output will be similarly imperfect.

  • Narrative leads to errors: This is the corollary of the context issue. Everything seems to be part of a story, and how that story is told often leads to critical errors. Phrases with a mathematical component to them -- stall speed, muddle through, Minsky moment, escape velocity, etc. -- can lead to lead to rich tales filled with emotional resonance. In model creation, this can be a disaster.

  • Confusing correlation with causation: The oldest statistical foible in the book. Look no further than the Fed’s obsession with the wealth effect -- consumers spend more because they either are, or perceive themselves to be, better off -- for a classic correlation error.”

The Farce of World Cup and Investment Models
4 Jul 16, 2014 8:17 AM EDT
By Barry Ritholtz

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