I've noticed an interesting problem with empirical likelihood that although Owen recognizes it is one of its shortfalls, I am facing it first hand. One of the advantages of EL is that there are no distributional assumptions placed on the data or the parameters. Therefore, EL is well suited for problems that have nonstandard distributions. However, theoretically, EL inference can only be conducted if the hypothesized values lie in the convex hull of the data. Without going into too much detail, the more skewed the data is, the more unlikely it is that a hypothesized value of the parameter would lie in the convex hull of the data. When the hypothesized parameter value is not in the convex hull, optimization becomes infeasible. This leads to an interesting conclusion: the strength of EL is also one of its major downfalls.

On another note, next week is midterm evaluations and I think I will be looking to alter my second half plans. I would like to include EL IV regression. More to come on that...

## No comments:

## Post a Comment