Reducing variance in linear regression

Question

While reading The Elements of Statistical Learning the author states that by shriking the coefficients of a liinear regression you raise the bias while lowering the variance and thus, sometimes, reducing the error of the prediction. But I don't quite get why that necessarily happens, I understand the bias variance tradeoff but can't grasp the direct relationship of the parameters of the model to these actual statistics.