HAC {gmm}R Documentation

Covariance matrix of weakly dependent time series

Description

Function to compute a consistent covariance matrix of the sample mean of a random vector of time series

Usage

HAC(x, weights = weightsAndrews2, prewhite = FALSE, ar.method = "ols",kernel=c("Quadratic Spectral", 
    "Truncated", "Bartlett", "Parzen", "Tukey-Hanning")) 

Arguments

x a ntimes q matrix of time series, where n is the sample size.
weights The method to compute the kernel weights. For now, weightsAndrews2 is the only one possible. I leave the option there because I am planning to give more choices in futur versions of the package.
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.
ar.method character. The method argument passed to ar for prewhitening.
kernel The choice of kernel

Details

HAC is simply a modified version of meatHAC from the package sandwich. The modifications have been made so that the argument x can be a matrix instead of an object of class lm or glm. The details on how is works can be found on the sandwich manual.

Value

A q times q matrix containing an estimator of the asymptotic variance of sqrt{n} bar{x}, where bar{x} is qtimes 1 vector with typical element bar{x}_i = frac{1}{n}sum_{j=1}^nx_{ji}. This function is called by gmm() but can also be used by itself.

References

Zeileis A (2006), Object-oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1–16. URL http://www.jstatsoft.org/v16/i09/.

Examples

x <- arima.sim(n=200,list(order=c(1,0,1),ar=.5,ma=.3,sd=.5))
y <- .4*x+rnorm(200)
x <- cbind(x,y)
vcov <- HAC(x)

[Package gmm version 0.1-0 Index]