sandwich (version 2.4-0)

vcovPC: Panel-Corrected Covariance Matrix Estimation

Description

Estimation of sandwich covariances a la Beck and Katz (1995) for panel data.

Usage

vcovPC(x, cluster = NULL, order.by = NULL,
  pairwise = FALSE, sandwich = TRUE, fix = FALSE, …)
meatPC(x, cluster = NULL, order.by = NULL,
  pairwise = FALSE, kronecker = TRUE, …)

Arguments

x

a fitted model object.

cluster

a variable indicating the clustering of observations or a list (or data.frame) thereof. By default, either attr(x, "cluster") is used.

order.by

a variable indicating the aggregation within time periods.

pairwise

logical. For unbalanced panels. Indicating whether the meat should be estimated pair- or casewise.

sandwich

logical. Should the sandwich estimator be computed? If set to FALSE only the meat matrix is returned.

fix

logical. Should the covariance matrix be fixed to be positive semi-definite in case it is not?

kronecker

logical. Calculate the meat via the Kronecker-product, shortening the computation time for small matrices. For large matrices, set kronecker = FALSE.

…

arguments passed to the meatPC or estfun function, respectively.

Value

A matrix containing the covariance matrix estimate.

Details

vcovPC is a function for estimating Beck and Katz (1995) panel-corrected covariance matrix.

The function meatPC is the work horse for estimating the meat of Beck and Katz (1995) covariance matrix estimators. vcovPC is a wrapper calling sandwich and bread (Zeileis 2006).

Following Bailey and Katz (2011), there are two alternatives to estimate the meat for unbalanced panels. For pairwise = FALSE, a balanced subset of the panel is used, whereas for pairwise = TRUE, a pairwise balanced sample is employed.

References

Bailey D & Katz JN (2011). “Implementing Panel-Corrected Standard Errors in R: The pcse Package”, Journal of Statistical Software, Code Snippets, 42(1), 1--11. URL http://www.jstatsoft.org/v42/c01/

Beck N & Katz JN (1995). “What To Do (and Not To Do) with Time-Series-Cross-Section Data in Comparative Politics”, American Political Science Review, 89(3), 634--647. URL http://www.jstor.org/stable/2082979

Zeileis A (2004). “Econometric Computing with HC and HAC Covariance Matrix Estimator”, Journal of Statistical Software, 11(10), 1--17. 10.18637/jss.v011.i10

Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators”, Journal of Statistical Software, 16(9), 1--16. 10.18637/jss.v016.i09

Examples

# NOT RUN {
## Petersen's data
data("PetersenCL", package = "sandwich")
m <- lm(y ~ x, data = PetersenCL)

## Beck and Katz (1995) standard errors
## balanced panel
sqrt(diag(vcovPC(m, cluster = PetersenCL$firm, order.by = PetersenCL$year)))

## unbalanced panel
PU <- subset(PetersenCL, !(firm == 1 & year == 10))
pu_lm <- lm(y ~ x, data = PU)
sqrt(diag(vcovPC(pu_lm, cluster = PU$firm, order.by = PU$year, pairwise = TRUE)))
sqrt(diag(vcovPC(pu_lm, cluster = PU$firm, order.by = PU$year, pairwise = FALSE)))
# }