I have been wondering, why are LASSO and LARS model selection methods so popular even though they are basically just variations of step-wise forward selection (and thus suffer from path dependency)?
Similarly, why are General to Specific (GETS) methods for model selection mostly ignored, even though they do better than LARS/LASSO because they don't suffer from the step-wise regression problem? (basic reference for GETS: http://www.federalreserve.gov/pubs/ifdp/2005/838/ifdp838.pdf - newst algorithm in this starts with a broad model and tree search that avoids path dependency, and has been shown to often do better than LASSO/LARS).
It just seems strange, LARS/LASSO seem to get so much more exposure and citations than General to Specific (GETS), anyone have any thoughts?
Not trying to start a heated debate, more looking for a rational explanation for why the literature does seem to focus on LASSO/LARS rather than GETS and few people actually point out shortcomings of LASSO/LARS.