I guess that "works" if you have more observations that you can use, which isn't the case for everyone. It "works" only in the sense that SAS can now do the mathematical calculations and you don't get the error, it doesn't work in the sense that you get good estimates or predictions. The problem with 387 prdictor variables remains. The problem is that these 387 predictor variables are still partially correlated with one another, possibly highly correlated with one another, and this causes regression to produce predictions and parameter estimates with very high mean square error, meaning that they are probably not good predictions and estimates. In which case, partial least squares provides better (lower mean square error, often dramatically lower mean square error) estimates and predictions.
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