Recursive methods for estimating multiple missing values of a multivariate stationary process

Existing methods for estimating linearly s future values of a m-variate stationary random process using a record of p vectors from the past consist in first solving the one-step prediction problem and then all the h-step prediction problems for 2⩽h⩽s independently. When the Levinson (1947) algorithm is used, each prediction problem is solved with a numerical complexity proportional to p². We propose new methods to solve the h-step prediction problems for h⩾2 with a numerical complexity proportional to p