# vcovHC

From sandwich v0.1-1
by Achim Zeileis

##### Heteroskedasticity-Consistent Covariance Matrix Estimation

Heteroskedasticity-consistent estimation of the covariance matrix of the coefficient estimates in a linear regression model.

- Keywords
- regression, ts

##### Usage

```
vcovHC(x, order.by = NULL, data = list(),
type = c("HC2", "const", "HC", "HC1", "HC3"))
```

##### Arguments

- x
- a fitted model object of class
`"lm"`

. - order.by
- formula. A formula with a single explanatory
variable like
`~ x`

. The observations in the model are ordered by the size of`x`

. If set to`NULL`

(the default) the observations are assumed to be ordered (e.g. a - data
- an optional data frame containing the variables in the
`order.by`

model. By default the variables are taken from the environment which`vcovHC`

is called from. - type
- a character string specifying the estimation type. For details see below.

##### Details

When `type = "const"`

constant variances are assumed and
and `covHC`

gives the usual estimate of the covariance matrix of
the coefficient estimates:

$$\hat \sigma^2 (X^\top X)^{-1}$$

All other methods do not assume constant variances and are suitable in case of
heteroskedasticity. `"HC"`

gives White's estimator; for details see the
references.

##### Value

- A matrix containing the covariance matrix estimate.

##### References

MacKinnon J. G., White H. (1985),
Some heteroskedasticity-consistent
covariance matrix estimators with improved finite sample properties.
*Journal of Econometrics* **29**, 305-325

##### See Also

##### Examples

```
## generate linear regression relationship
## with homoskedastic variances
x <- sin(1:100)
y <- 1 + x + rnorm(100)
## compute usual covariance matrix of coefficient estimates
fm <- lm(y ~ x)
vcovHC(fm, type="const")
vcov(fm)
sigma2 <- sum(residuals(lm(y~x))^2)/98
sigma2 * solve(crossprod(cbind(1,x)))
```

*Documentation reproduced from package sandwich, version 0.1-1, License: GPL version 2*

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