loc.cov.unc(uncspec, lag, req.length = uncspec$TT - lag, smooth = T, kernel = "normal", g = 50, extrap.lag = 1)

Computes the local autocovariance from the uncorrected Haar spectrum of a process.

uncspec The uncorrected Haar spectrum of a process. To obtain the spectrum, use uncor.spec.
lag Autocovariance lag. Setting lag = 0 gives the local variance.
req.length The required length of the local autocovariance vector. The routine returns req.length values of the local autocovariance at the end of the series.
smooth Logical switch: T smooths the local autocovariance, F leaves it unsmoothed.
kernel The kernel to be used in smoothing. Like in ksmooth, can be set to "box", "triangle", "parzen" or "normal". Also see help(ksmooth) in S-Plus.
g The value of the bandwidth for kernel smoothing.
extrap.lag Apart from the vector of local autocovariances, the routine also returns an extrapolated autocovariance Cov(x[t+extrap.lag], x[t+extrap.lag-lag]), where uncspec = uncor.spec(x) and t is the length of x. This value is needed for the Yule-Walker matrix constructed using predeq.est. Here, the standard kernel extrapolation is used (see help(ksmooth) is S-Plus).

lcov The vector (of length req.length) of the local autocovariances at the end of the series.
extrapolated The extrapolated autocovariance (see above).