can be estimated as follows. For the model where , one may write Using in-sample data values, the first term on the right side is equivalent to Thus, If is known or well-estimated by , it becomes possible to estimate MSPE by Colin Mallows advocated this method in the construction of his model selection statistic Cp, which is a normalized version of the estimated MSPE: where p the number of estimated parameters p and is computed from the version of the model that includes all possible regressors. That concludes this proof. See also Mean squared error Errors and residuals in statistics Law of total variance Further reading Point estimation performance Statistical deviation and dispersion Loss functions