What is the difference between calibration and estimation

Some parameter values may simply be fixed to "sensible" levels, e. Some other parameters may be inferred from the steady state of the model matching sample average of observables. The rest of the parameters may be estimated using "proper" statistical procedures. Finally Bayesian approaches are popular, because sample sizes tend to be small, and the usual asymptotic results for classical methods may not apply.

Fixing some values to sensible microeconomic levels can simply be seen as setting a unit point mass prior in this framework. Economist 68a6. I honestly always thought calibrate meant you took a lot of parameters from the literature so pointless to do formal statistical estimation on remaining parameters. So it becomes like a quasi-minimum distance, as determined by the researcher. Economist 2d4d. Economist 7fff. Based on this, please explain to me what's the difference between calibration and maximul likelihood estimation.

So: estimation will set kind, structure and coefficients. Calibration will tweak coefficients, holding kind and structure constant. Newton's model of motion is fine for most purposes. By calibrating the gravitational coefficient in it, we can make estimates of the mass of the Earth.

But it won't work as a model of relativistic motion - that needs the estimation of a different model: there is no recalibration of Newton's model that works for relativistic motion - no coeffecient will work, because the model itself is simply the wrong kind and structure.

It omits mechanisms and responses that are absolutely crucial, if the model is to be useful. Similarly with economic models, Paul Krugman's point is that freshwater economists are saying that their model structures are fine, just the coefficients need tweaking. The problem with that is that if their structures are wrong, no amount of tweaking will make the models useful.

Only by going back to basics, and re-estimating the whole model, would they incorporate the crucial mechanisms and responses. He argues that they won't do that, because that would require them to recognise that their existing paradigm is inadequate. For example, one creates a model to estimate sales of a product in a store at a given day of the year. The forecast for most of the year look plausible, but the estimation looks wrong for Christmas season for example, the sales are on the similar level as in November, but they should be bigger.

One then calibrates the model, perhaps changing or adding some new variables, so the forecast for December will be bigger than the ones previously received. Share this: Twitter Facebook. Like this: Like Loading Published by seandaddy. Published April 11, September 10, Previous Post Principles of constructing quantitative models.

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