vcovSCC {plm}

Driscoll and Kraay (1998) Robust Covariance Matrix Estimator

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 Driscoll and Kraay (1998) 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 MacKinnon and White (1985) and Cribari–Neto (2004) (see Zeileis 2004)).

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 also Zeileis 2004), 4.1-2, and examples below.

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

Cribari–Neto F (2004). “Asymptotic Inference Under Heteroskedasticity of Unknown Form.” Computational Statistics & Data Analysis, 45, 215–233.

Driscoll JC, Kraay AC (1998). “Consistent covariance matrix estimation with spatially dependent panel data.” Review of economics and statistics, 80(4), 549–560.

Hoechle D (2007). “Robust standard errors for panel regressions with cross-sectional dependence.” Stata Journal, 7(3), 281-312. https://ideas.repec.org/a/tsj/stataj/v7y2007i3p281-312.html.

MacKinnon JG, White H (1985). “Some Heteroskedasticity–Consistent Covariance Matrix Estimators With Improved Finite Sample Properties.” Journal of Econometrics, 29, 305–325.

Zeileis A (2004). “Econometric Computing With HC and HAC Covariance Matrix Estimators.” Journal of Statistical Software, 11(10), 1–17. https://www.jstatsoft.org/article/view/v011i10.

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)