Conditional Prediction Intervals for Linear Regression

We construct prediction intervals for the linear regression model with IID errors with a known distribution, not necessarily Gaussian. The coverage probability of our prediction intervals is equal to the nominal confidence level not only unconditionally but also conditionally given a natural sigma-algebra of invariant events. This implies, in particular, the perfect calibration of our prediction intervals in the on-line mode of prediction.