*n*is smaller than number of estimated parameters

*k*. Traditionally, we have to accept that this problem is unsolvable. But with the aid of priors, we can actually increase

*k*number of observations. So now the degree of freedom becomes

*n+k-k > k*. The model is identifiable. I have mentioned about bayesglm here which is exactly developed for this problem. bayesglm is now in R package: arm.

When is it a problem? Normally, when we are dealing with a fixed-effect model, this would be a potential problem. Traditionally, dealing with the fixed effect model, we add dummies into the model. If we have small

*n*, but many individual units

*a*(e.g., countries), we will encounter the problem that

*n-(k+a) <>. If we have priors for*

*(k+a)*parameters, we can identify the model as*n + (k+a) - (k+a) > 0*.
## No comments:

Post a Comment