Hi everyone! I want to ask a question about modeling a multi-wave data structure.

Suppose that you have a panel dataset consisting of three waves, but the variable of interest is measured in Wave 3. So it is not possible to use the usual panel models. I would like to estimate this Wave 3 outcome by using Wave 1 and Wave 2 predictors simultaneously, instead of just using variable from only one wave. What would be the best way of handling this?

Using both can cause (a) high collinearity and (b) kitchen-sink-type long regression tables. I just want to make use of the information as much as possible.

Thanks in advance!

EDIT: I asked this question before a year ago, but the wording & the definition of the problem was highly problematic (I was referring to it as "panel model", which is not). I hope it is OK to repost it, as I cannot edit it.