AppliedMultiple choice
With highly collinear regressors, ridge regression often predicts better out of sample than OLS. Why?
- ABecause ridge estimates are unbiased while OLS estimates are biased
- BIt accepts a little bias for a large cut in variance
- CBecause ridge removes the collinear variables from the model entirely
- DBecause ridge fits the training data more closely than OLS
The worked solution is in Premium
The answer, the full working and the one idea to take away – for this and all 1,322 questions in the bank. Answer it in practice and your working is marked, with a known mistake named when you make one.
Learn the method
More regression and econometrics questions
- With orthonormal regressors, the OLS coefficient is 2 and the ridge penalty is λ = 3.Applied
- With orthonormal regressors, the lasso minimising…Applied
- Two nearly identical factors, part 2 of 3Applied
- A design matrix has squared singular values 9, 4 and 1.Advanced
- Two nearly identical factors, part 3 of 3Advanced
- One influential observation, part 1 of 3Foundation