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Econometric disillusionment

Summary:
Because I was there when the economics department of my university got an IBM 360, I was very much caught up in the excitement of combining powerful computers with economic research. Unfortunately, I lost interest in econometrics almost as soon as I understood how it was done. My thinking went through four stages: 1. Holy shit! Do you see what you can do with a computer’s help. 2. Learning computer modeling puts you in a small class where only other members of the caste can truly understand you. This opens up huge avenues for fraud: 3. The main reason to learn stats is to prevent someone else from committing fraud against you. 4. More and more people will gain access to the power of statistical analysis. When that happens, the stratification of importance within the

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Econometric disillusionment

Because I was there when the economics department of my university got an IBM 360, I was very much caught up in the excitement of combining powerful computers with economic research. Unfortunately, I lost interest in econometrics almost as soon as I understood how it was done. My thinking went through four stages:

1. Holy shit! Do you see what you can do with a computer’s help.
2. Learning computer modeling puts you in a small class where only other members of the caste can truly understand you. This opens up huge avenues for fraud:
3. The main reason to learn stats is to prevent someone else from committing fraud against you.
4. More and more people will gain access to the power of statistical analysis. When that happens, the stratification of importance within the profession should be a matter of who asks the best questions.

Disillusionment began to set in. I began to suspect that all the really interesting economic questions were FAR beyond the ability to reduce them to mathematical formulas. Watching computers being applied to other pursuits than academic economic investigations over time only confirmed those suspicions.

1. Precision manufacture is an obvious application for computing. And for many applications, this worked magnificently. Any design that combined straight line and circles could be easily described for computerized manufacture. Unfortunately, the really interesting design problems can NOT be reduced to formulas. A car’s fender, for example, cannot be described​ using formulas—it can only be described by specifying an assemblage of multiple points. If math formulas cannot describe something as common and uncomplicated as a car fender, how can it hope to describe human behavior?
2. When people started using computers for animation, it soon became apparent that human motion was almost impossible to model correctly. After a great deal of effort, the animators eventually put tracing balls on real humans and recorded that motion before transferring it to the animated character. Formulas failed to describe simple human behavior—like a toddler trying to walk.

Lately, I have discovered a Swedish economist who did NOT give up econometrics merely because it sounded so impossible. In fact, he still teaches the stuff. But for the rest of us, he systematically destroys the pretensions of those who think they can describe human behavior with some basic Formulas.

Jonathan Larson

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Lars Pålsson Syll
Professor at Malmö University. Primary research interest - the philosophy, history and methodology of economics.

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