Ted Sargent über „Kalibrierung“

Ted Sargent ist in aller Munde, weil er den diesjährigen Nobelpreis Preis für Wirtschaftswissenschaften der schwedischen Reichsbank im Gedenken an Alfred Nobel gewonnen hat. Das ist gut so, denn der Mann viel Interessantes zu sagen. Brad DeLong zitiert aus einem Interview mit Sargent:

Evans and Honkapohja: What were the profession’s most important responses to the Lucas Critique?

Sargent: There were two. The first and most optimistic response was complete rational expectations econometrics. A rational expectations equilibrium is a likelihood function. Maximize it.

Evans and Honkapohja: Why optimistic?

Sargent: You have to believe in your model to use the likelihood function. it provides a coherent way to estimate objects of interest (preferences, technologies, information sets, measurement processes) within the context of a trusted model.

Evans and Honkapohja: What was the second response?

Sargent: Various types of calibration. Calibration is less optimistic about what your theory can accomplish because you would only use it if you din’t fully trust your entire model, meaning that you think your model is partly misspecified or incompetely specified, or if you trusted someone else’s model and data set more than your own. My recollection is that Bob Lucas and Ed Prescott were initially very enthusiastic about rational expetations econometrics. After all, it simply involved imposing on ourselves the same high standards we had criticized the Keynesians for failing to live up to. But after about five years of doing likelihood ratio tests on rational expectations models, I recall Bob Lucas and Ed Prescott both telling me that those tests were rejecting too many good models. The idea of calibration is to ignore some of the probabilistic implications of your model but to retain others. Somehow, calibration was intended as a balanced response to professing that your model, although not correct, is still worthy as a vehicle for quantitative policy analysis….

Evans and Honkapohja: Do you think calibration in macroeconomics was an advance?

Sargent: In many ways, yes. I view it as a constructive response to Bob‘ remark that „your likelihood ratio tests are rejecting too many good models“. In those days… there was a danger that skeptics and opponents would misread those likelihood ratio tests as rejections of an entire class of models, which of course they were not…. The unstated cse for calibration was that it was a way to continue the process of acquiring experience in matching rational expectations models to data by lowering our standards relative to maximum likelihood, and emphasizing those features of the data that our models could capture. Instead of trumpeting their failures in terms of dismal likelihood ratio statistics, celebrate the featuers that they could capture and focus attention on the next unexplained feature that ought to be explained. One can argue that this was a sensible response… a sequential plan of attack: let’s first devote resources to learning how to create a range of compelling equilibrium models to incorporate interesting mechanisms. We’ll be careful about the estimation in later years when we have mastered the modelling technology…

Das ist mal wieder Futter für alle, die der Ökonomie ihren Status als Wissenschaft absprechen wollen. Lucas und Prescott stellen fest: „those tests were rejecting too many good models“. Und die Reaktion darauf ist, dass man die Modelle behält und andere Tests entwickelt? Das erinnert mich ein bisschen an die Fans der Homöopathie. Die lassen sich auch nicht von der Tatsache abschrecken, dass wissenschaftliche Experimente keine Evidenz für deren Wirksamkeit erbringen.



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