About Models

I did not intend to write this, but I felt I need to after reading a comment to my post.

There are two kinds of models. One kind of model is to make a point, or formalize an insight. The other kind of model is used to model the reality closely, and they are intended to be brought to empirical tests.

The first kind of model are designed to be as simple as possible, but not simpler. The second kind is designed to be as realistic as possible. We calibrate the parameters, and estimate the distributions if there is stochastic elements. Each parameter or variable has a direct implications---none of which is true for the first model.

I probably will only discuss and present the first kind of model in my blogs (I tend to prefer them in research as well). So what to make of my model? There are some basic take-aways, and that is why I wrote the model down. There are also many conclusions specific to the model assumptions, do not pay attention to them. They are not likely to be robust.  The general rule is that do not be religious of the numbers I generate, and they depend on model assumptions heavily. Nevertheless, I try to present robust qualitative results.

There are tons of criticisms that I could leash out against normal distribution. But I will use them anyway. It is unlikely it gonna change the basic idea, and it is convenient.


I will end with a semi-relevant anecdote. In class one day, professor Helpman decided to present a general model of multiple goods. There is one "theoretic" guy in class and he asked:" Why don't we make the model more realistic and generalize to a continuum of goods?" Not a super impressive question. With finite numbers of molecules in the universe (yes, a large number),  "continuum" is is not more realistic than "general N".