On Calibration and Nobel Prize Winner Thomas Sargent

19 oktober, 2011 kl. 19:35 | Publicerat i Statistics & Econometrics, Theory of Science & Methodology | Kommentarer inaktiverade för On Calibration and Nobel Prize Winner Thomas Sargent

In an interview – by Seppo Honkapohja and Lee Evans in Macroeconomic Dynamics (2005, vol.9, 561-583) – Thomas Sargent says:

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…

But is the Lucas/Kydland/Prescott/Sargent calibration really an advance?

Let’s see what two eminent econometricians have to say. In Journal of Economic Perspective (1996, vol. 10, 87-104) Lars Peter Hansen and James J. Hickman writes:

It is only under very special circumstances that a micro parameter such as the intertemporal elasticity of substitution or even a marginal propensity to consume out of income can be ‘plugged into’ a representative consumer model to produce an empirically concordant aggregate model … What credibility should we attach to numbers produced from their ‘computational experiments’, and why should we use their ‘calibrated models’ as a basis for serious quantitative policy evaluation? … There is no filing cabinet full of robust micro estimats ready to use in calibrating dynamic stochastic equilibrium models … The justification for what is called ‘calibration’ is vague and confusing.

This is the view of econometric methodologist Kevin Hoover :

The calibration methodology, to date,  lacks any discipline as stern as that imposed by econometric methods.

And, finally, this is the verdict of Paul Krugman :

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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