weightsLumley {sandwich} | R Documentation |

A set of functions implementing a class of kernel-based heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).

weave(x, order.by = NULL, prewhite = FALSE, C = NULL, method = c("truncate", "smooth"), acf = isoacf, adjust = FALSE, diagnostics = FALSE, sandwich = TRUE, tol = 1e-7, data = list(), ...) weightsLumley(x, order.by = NULL, C = NULL, method = c("truncate", "smooth"), acf = isoacf, tol = 1e-7, data = list(), ...)

`x` |
a fitted model object of class `"lm"` or `"glm"` . |

`order.by` |
Either a vector `z` or a formula with a single explanatory
variable like `~ z` . The observations in the model
are ordered by the size of `z` . If set to `NULL` (the
default) the observations are assumed to be ordered (e.g., a
time series). |

`prewhite` |
logical or integer. Should the estimating functions
be prewhitened? If `TRUE` or greater than 0 a VAR model of
order `as.integer(prewhite)` is fitted via `ar` with
method `"ols"` and `demean = FALSE` . |

`C` |
numeric. The cutoff constant `C` is by default
4 for method `"truncate"` und 1 for method `"smooth"` . |

`method` |
a character specifying the method used, see details. |

`acf` |
a function that computes the autocorrelation function of
a vector, by default `isoacf` is used. |

`adjust` |
logical. Should a finite sample adjustment be made? This amounts to multiplication with $n/(n-k)$ where $n$ is the number of observations and $k$ the number of estimated parameters. |

`diagnostics` |
logical. Should additional model diagnostics be returned?
See `vcovHAC` for details. |

`sandwich` |
logical. Should the sandwich estimator be computed?
If set to `FALSE` only the middle matrix is returned. |

`tol` |
numeric. Weights that exceed `tol` are used for computing
the covariance matrix, all other weights are treated as 0. |

`data` |
an optional data frame containing the variables in the `order.by`
model. By default the variables are taken from the environment which
the function is called from. |

`...` |
currently not used. |

`weave`

is a convenience interface to `vcovHAC`

using
`weightsLumley`

: first a weights function is defined and then `vcovHAC`

is called.

Both weighting methods are based on some estimate of the autocorrelation
function *r* (as computed by `acf`

) of the residuals of
the model `x`

. The weights for the `"truncate"`

method are

*I{n * r ** 2 > C}*

and the weights for the `"smooth"`

method are

*min{1, C * n * r ** 2}*

where n is the number of observations in the model an C is the truncation
constant `C`

.

Further details can be found in Lumley & Heagerty (1999).

`weave`

returns the same type of object as `vcovHAC`

which is typically just the covariance matrix.

`weightsLumley`

returns a vector of weights.

Lumley A & Heagerty P (1999),
Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression.
*Journal of the Royal Statistical Society B*, **61**,
459–477.

`vcovHAC`

, `weightsAndrews`

,
`kernHAC`

x <- sin(1:100) y <- 1 + x + rnorm(100) fm <- lm(y ~ x) weave(fm) vcov(fm)

[Package *sandwich* version 1.1-1 Index]