jueves, 20 de octubre de 2011

A esto me dedico, y así lo veo

Hoy mi entrada es "un poco heavy" para lo que es habitual en este blog. Pero es que he leído lo que el Nobel Paul Krugman piensa sobre la técnica metodológica que yo uso en mi investigación (modelos de equilibrio general aplicado), y no puedo menos que ponerlo, porque refleja también mi visión. Con todo lo polémico (e inteligente, y con un gigantesco conocimiento de Economía) que es Krugman, esta visión que da me proporciona cierta "seguridad intelectual", y me reafirma en ver estos complejos modelos como lo que son: representaciones muy muy muy simplificadas de la realidad en la que vivimos (y prometo para mañana una entrada divertida de la nueva idea proteccionista que los super-imaginativos argentinos han puesto en práctica):

In one of my home fields, international trade, there’s fairly widespread use of “computable general equilibrium” models — fully internally consistent models about how prices and quantities fit together, with some parameters that are guesstimates based on the literature, and others that are tweaked so as to match actual trade flows in some base period. These models are then used for “what-if” analyses, especially the effects of possible changes in trade policy.

I think this is an acceptable practice, as long as you keep your perspective. The models clearly aren’t literally true, and in no sense are you testing your theory. What you’re basically doing is elaborate thought experiments that are somewhat disciplined by the data, and which you hope are more informative than just plain guesses.

The point is that if you have a conceptual model of some aspect of the world, which you know is at best an approximation, it’s OK to see what that model would say if you tried to make it numerically realistic in some dimensions.

But doing this gives you very little help in deciding whether you are more or less on the right analytical track. I was going to say no help, but it is true that a calibration exercise is informative when it fails: if there’s no way to squeeze the relevant data into your model, or the calibrated model makes predictions that you know on other grounds are ludicrous, something was gained. But no way is calibration a substitute for actual econometrics that tests your view about how the world works.

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