`weightsLumley.Rd`

A set of functions implementing weighted empirical adaptive variance estimation (WEAVE) as introduced by Lumley and Heagerty (1999). This is implemented as a special case of the general class of kernel-based heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991), using a special choice of weights.

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. |
---|---|

order.by | Either a vector |

prewhite | logical or integer. Should the estimating functions
be prewhitened? If |

C | numeric. The cutoff constant |

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

acf | a function that computes the autocorrelation function of
a vector, by default |

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 |

sandwich | logical. Should the sandwich estimator be computed?
If set to |

tol | numeric. Weights that exceed |

data | an optional data frame containing the variables in the |

... | 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 \(\rho\) (as computed by `acf`

) of the residuals of
the model `x`

. The weights for the `"truncate"`

method are

$$I\{n \rho^2 > C\}$$

and the weights for the `"smooth"`

method are

$$\min\{1, C n \rho^2\}$$

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

x <- sin(1:100) y <- 1 + x + rnorm(100) fm <- lm(y ~ x) weave(fm) #> (Intercept) x #> (Intercept) 0.0113474272 -0.0002016924 #> x -0.0002016924 0.0205963399 vcov(fm) #> (Intercept) x #> (Intercept) 1.157960e-02 2.929455e-05 #> x 2.929455e-05 2.303555e-02