  Suppose we observe an invertible linear process with independent mean-zero
innovations and with coefficients depending on a finite-dimensional parameter,
and we want to estimate the expectation of some function under the stationary
distribution of the process. The usual estimator would be the empirical
estimator. It can be improved using the fact that the innovations are centered.
  We construct an even better estimator using the representation of the
observations as infinite-order moving averages of the innovations. Then the
expectation of the function under the stationary distribution can be written as
the expectation under the distribution of an infinite series in terms of the
innovations, and it can be estimated by a U-statistic of increasing order
  (also called an ``infinite-order U-statistic'') in terms of the estimated
innovations. The estimator can be further improved using the fact that the
innovations are centered. This improved estimator is optimal if the
coefficients of the linear process are estimated optimally.
