Ridge regression for multicollinearity and outliers I'm wondering about techniques like ridge regression with regard to both multicollinearity and outliers.
My understanding is that ridge regression is primarily used for multicollinearity, but that somehow it is robust to outliers.
If you have a multicollinearity problem, what exactly is it that ridge regression gives you? Isn't the solution to remove the problematic variables? Does ridge regression simply make one of your covariates non-significant and signify that you should remove it?
If you have an outlier problem, what does it mean that ridge regression is robust to outliers? I tried it and using ridge regression gave me more outliers in terms of standardized residual diagnostics than OLS.

A ridge regression is more of an alternative to a robust standard errors regression where heteroskedasticity (shape of the errors) is of an hour glass shape rather than rising in magnitude - the equivalent of a centred around a mean or zero and maybe a non-negative number type variance. Neither regression would really much help with multicolinearity, they are built for variance in the errors. (Outliers end up in the error term).
What I would do is look at a scatterplot of your errors and decide to use a robust regression or a ridge regression, based on the above.
If your t values are abnormally high, larger than 10 or even up to 20-30, then I would assume some multicolinearity has taken place. That would make your estimates untrustworthy, not least to other researchers. The best option is to use a scatterplot matrix of the results and use only one independent variable from each set of independent variables that have a clear correlation. The choice of which can be through model fitting or using a theoretical framework based on which variable is higher in the causal chain according to Gauss-Markov theorem.
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