where w(t, N n 0 ) is a weight function ("lag window"), which may depend on N n 0 . w(t, N n 0 ) is greater than or equal to 0 and usually no larger than 1. This reduces the noise from later autocovariance terms by down-weighting them. Larger lag windows will generally underestimate the autocovariance less, at the expense of more noise.