predeq.est

predeq.est

predeq.est(uncspec, h = 1, p = uncspec$TT, smooth = T, kernel = "normal", g = 30, max.lag = p - 1)

Constructs the Yule-Walker matrix and the right-hand side of the prediction equations.

Takes:

uncspec The uncorrected Haar spectrum of a process. To obtain the spectrum, use uncor.spec.

h Prediction lag.

p Number of rows and columns of the Yule-Walker matrix.

smooth Logical switch: T smooths the local autocovariance before putting in in the matrix, 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.

max.lag The local autocovariances at lags 0, 1, ..., max.lag will be put in the matrix as they have been estimated. For larger lags, they will be set to zero!

Returns:

B The Yule-Walker matrix.

RHS The right-hand side of the prediction equations.

extra.var The extrapolated variance Var(x[t+h]), where uncspec = uncor.spec(x) and t is the length of x. It is required by sig.sq to compute the standard prediction error.

uncspec	The uncorrected Haar spectrum of a process. To obtain the spectrum, use uncor.spec.
h	Prediction lag.
p	Number of rows and columns of the Yule-Walker matrix.
smooth	Logical switch: T smooths the local autocovariance before putting in in the matrix, 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.
max.lag	The local autocovariances at lags 0, 1, ..., max.lag will be put in the matrix as they have been estimated. For larger lags, they will be set to zero!

B	The Yule-Walker matrix.
RHS	The right-hand side of the prediction equations.
extra.var	The extrapolated variance Var(x[t+h]), where uncspec = uncor.spec(x) and t is the length of x. It is required by sig.sq to compute the standard prediction error.