vcovSCC | R Documentation |

Nonparametric robust covariance matrix estimators *a la
Driscoll and Kraay* for panel models with cross-sectional
*and* serial correlation.

```
vcovSCC(x, ...)
## S3 method for class 'plm'
vcovSCC(
x,
type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
cluster = "time",
maxlag = NULL,
inner = c("cluster", "white", "diagavg"),
wj = function(j, maxlag) 1 - j/(maxlag + 1),
...
)
## S3 method for class 'pcce'
vcovSCC(
x,
type = c("HC0", "sss", "HC1", "HC2", "HC3", "HC4"),
cluster = "time",
maxlag = NULL,
inner = c("cluster", "white", "diagavg"),
wj = function(j, maxlag) 1 - j/(maxlag + 1),
...
)
```

`x` |
an object of class |

`...` |
further arguments |

`type` |
the weighting scheme used, one of |

`cluster` |
switch for vcovG; set at |

`maxlag` |
either |

`inner` |
the function to be applied to the residuals inside the
sandwich: |

`wj` |
weighting function to be applied to lagged terms, |

`vcovSCC`

is a function for estimating a robust covariance matrix
of parameters for a panel model according to the
\insertCiteDRIS:KRAA:98;textualplm method, which is consistent
with crossâ€“sectional and serial correlation in a T-asymptotic
setting and irrespective of the N dimension. The use with random
effects models is undocumented.

Weighting schemes specified by `type`

are analogous to those in
`sandwich::vcovHC()`

in package sandwich and are
justified theoretically (although in the context of the standard
linear model) by \insertCiteMACK:WHIT:85;textualplm and
\insertCiteCRIB:04;textualplm \insertCite@see @ZEIL:04plm).

The main use of `vcovSCC`

(and the other variance-covariance estimators
provided in the package `vcovHC`

, `vcovBK`

, `vcovNW`

, `vcovDC`

) is to pass
it to plm's own functions like `summary`

, `pwaldtest`

, and `phtest`

or
together with testing functions from the `lmtest`

and `car`

packages. All of
these typically allow passing the `vcov`

or `vcov.`

parameter either as a
matrix or as a function, e.g., for Waldâ€“type testing: argument `vcov.`

to
`coeftest()`

, argument `vcov`

to `waldtest()`

and other methods in the
lmtest package; and argument `vcov.`

to
`linearHypothesis()`

in the car package (see the
examples), \insertCite@see also @ZEIL:04plm, 4.1-2, and examples below.

An object of class `"matrix"`

containing the estimate of
the covariance matrix of coefficients.

Giovanni Millo, partially ported from Daniel Hoechle's (2007) Stata code

CRIB:04plm

\insertRefDRIS:KRAA:98plm

\insertRefHOEC:07plm

\insertRefMACK:WHIT:85plm

\insertRefZEIL:04plm

`sandwich::vcovHC()`

from the sandwich
package for weighting schemes (`type`

argument).

```
data("Produc", package="plm")
zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling")
## as function input to plm's summary method (with and without additional arguments):
summary(zz, vcov = vcovSCC)
summary(zz, vcov = function(x) vcovSCC(x, method="arellano", type="HC1"))
## standard coefficient significance test
library(lmtest)
coeftest(zz)
## SCC robust significance test, default
coeftest(zz, vcov.=vcovSCC)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovSCC(x, type="HC1", maxlag=4))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovSCC)
## Not run:
## test of hyp.: 2*log(pc)=log(emp)
library(car)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovSCC)
## End(Not run)
```

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