Description Usage Arguments Details Value Author(s) References See Also Examples

High-level convenience wrapper for double-clustering robust covariance
matrix estimators *a la* Thompson (2011) and Cameron, Gelbach and
Miller (2011) for panel models.

1 2 3 |

`x` |
an object of class |

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

`...` |
further arguments |

.

`vcovDC`

is a function for estimating a robust covariance matrix of
parameters for a panel model with errors clustering along both dimensions.
The function is a convenience wrapper simply summing a group- and a
time-clustered covariance matrix and subtracting a diagonal one *a la*
White.

Weighting schemes specified by `type`

are analogous to those in `vcovHC`

in package sandwich and are justified theoretically (although in the context of the standard linear model) by MacKinnon and White (1985) and Cribari-Neto (2004) (see Zeileis (2004)).

The main use of `vcovDC`

is to be an argument to other functions,
e.g. for Wald-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). Notice that the `vcov`

and `vcov.`

arguments allow to supply a function (which is the safest) or a matrix (see Zeileis (2004), 4.1-2 and examples below).

An object of class `"matrix"`

containing the estimate of the covariance matrix of coefficients.

Giovanni Millo

Cameron, A.C., Gelbach, J.B., & Miller, D.L. (2011) Robust inference with
multiway clustering, *Journal of Business and Economic Statistics*
**29(2)**, pp. 238–249.

Cribari-Neto, F. (2004) Asymptotic inference under heteroskedasticity
of unknown form. *Computational Statistics & Data Analysis*
**45(2)**, pp. 215–233.

MacKinnon, J. G. and White, H. (1985) Some heteroskedasticity-consistent
covariance matrix estimators with improved finite sample properties.
*Journal of Econometrics* **29(3)**, pp. 305–325.

Thompson, S.B. (2011) Simple formulas for standard errors that cluster by
both firm and time, *Journal of Financial Economics* **99(1)**, pp. 1–10.

Zeileis, A. (2004) Econometric Computing with HC and HAC Covariance Matrix
Estimators. *Journal of Statistical Software*, **11**(10), pp. 1–17.
URL http://www.jstatsoft.org/v11/i10/.

`vcovHC`

from the sandwich package for weighting schemes (`type`

argument).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
library(lmtest)
library(car)
data("Produc", package="plm")
zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, model="pooling")
## standard coefficient significance test
coeftest(zz)
## DC robust significance test, default
coeftest(zz, vcov.=vcovDC)
## idem with parameters, pass vcov as a function argument
coeftest(zz, vcov.=function(x) vcovDC(x, type="HC1", maxlag=4))
## joint restriction test
waldtest(zz, update(zz, .~.-log(emp)-unemp), vcov=vcovDC)
## test of hyp.: 2*log(pc)=log(emp)
linearHypothesis(zz, "2*log(pc)=log(emp)", vcov.=vcovDC)
``` |

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