vcovSCC: Driscoll and Kraay (1998) Robust Covariance Matrix Estimator

In plm: Linear Models for Panel Data

Description

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

Usage

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),
  ...
)

Arguments

x	an object of class "plm" or "pcce"
...	further arguments
type	the weighting scheme used, one of "HC0", "sss", "HC1", "HC2", "HC3", "HC4", see Details,
cluster	switch for vcovG; set at "time" here,
maxlag	either NULL or a positive integer specifying the maximum lag order before truncation
inner	the function to be applied to the residuals inside the sandwich: "cluster" for SCC, "white" for Newey-West, ("diagavg" for compatibility reasons)
wj	weighting function to be applied to lagged terms,

Details

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 cross–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 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), see \insertCite@ZEIL:04, 4.1-2 and examples belowplm.

Value

An object of class "matrix" containing the estimate of the covariance matrix of coefficients.

Author(s)

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

References

\insertRef

CRIB:04plm

\insertRef

DRIS:KRAA:98plm

\insertRef

HOEC:07plm

\insertRef

MACK:WHIT:85plm

\insertRef

ZEIL:04plm

See Also

sandwich::vcovHC() from the sandwich package for weighting schemes (type argument).

Examples

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)

plm documentation built on Sept. 21, 2021, 3:01 p.m.