Tools such as random forests or adaboost are powerful at solving cross-sectional binary logistic problems or prediction problems where there are many weak learners. But can these tools be adapted to solve panel regression problems?
One could naively introduce a time index as an independent variable but all this does is to provide an additional degree of freedom to the fitting algorithm. What we would like is a solution that allows information from period T-1 to have bearing on period T.
If there is not a straightforward way to do this using these algorithms, is there an alternative algorithm that can perform a panel regression making use of the information in both the cross-section and time-series?