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. |