Panel-Corrected Covariance Matrix Estimation
Estimation of sandwich covariances a la Beck and Katz (1995) for panel data.
vcovPC(x, cluster = NULL, order.by = NULL, pairwise = FALSE, sandwich = TRUE, fix = FALSE, ...) meatPC(x, cluster = NULL, order.by = NULL, pairwise = FALSE, kronecker = TRUE, ...)
x |
a fitted model object. |
cluster |
a variable indicating the clustering of observations,
a |
order.by |
a variable, list/data.frame, or formula indicating the
aggregation within time periods. By default |
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 |
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 |
... |
arguments passed to the |
vcovPC is a function for estimating Beck and Katz (1995)
panel-corrected covariance matrix.
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.
The cluster/order.by specification can be made in a number of ways:
Either both can be a single variable or cluster can be a
list/data.frame of two variables.
If expand.model.frame works for the model object x,
the cluster (and potentially additionally order.by) can also be
a formula. By default (cluster = NULL, order.by = NULL),
attr(x, "cluster") and attr(x, "order.by") are checked and
used if available. If not, every observation is assumed to be its own cluster,
and observations within clusters are assumed to be ordered accordingly.
If the number of observations in the model x is smaller than in the
original data due to NA processing, then the same NA processing
can be applied to cluster if necessary (and x$na.action being
available).
A matrix containing the covariance matrix estimate.
Bailey D, Katz JN (2011). “Implementing Panel-Corrected Standard Errors in R: The pcse Package”, Journal of Statistical Software, Code Snippets, 42(1), 1–11. doi: 10.18637/jss.v042.c01
Beck N, Katz JN (1995). “What To Do (and Not To Do) with Time-Series-Cross-Section Data in Comparative Politics”, American Political Science Review, 89(3), 634–647. doi: 10.2307/2082979
Zeileis A (2004). “Econometric Computing with HC and HAC Covariance Matrix Estimator”, Journal of Statistical Software, 11(10), 1–17. doi: 10.18637/jss.v011.i10
Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators”, Journal of Statistical Software, 16(9), 1–16. doi: 10.18637/jss.v016.i09
Zeileis A, Köll S, Graham N (2020). “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Journal of Statistical Software, 95(1), 1–36. doi: 10.18637/jss.v095.i01
## Petersen's data
data("PetersenCL", package = "sandwich")
m <- lm(y ~ x, data = PetersenCL)
## Beck and Katz (1995) standard errors
## balanced panel
sqrt(diag(vcovPC(m, cluster = ~ firm + year)))
## unbalanced panel
PU <- subset(PetersenCL, !(firm == 1 & year == 10))
pu_lm <- lm(y ~ x, data = PU)
sqrt(diag(vcovPC(pu_lm, cluster = ~ firm + year, pairwise = TRUE)))
sqrt(diag(vcovPC(pu_lm, cluster = ~ firm + year, pairwise = FALSE)))
## the following specifications of cluster/order.by are equivalent
vcovPC(m, cluster = ~ firm + year)
vcovPC(m, cluster = PetersenCL[, c("firm", "year")])
vcovPC(m, cluster = ~ firm, order.by = ~ year)
vcovPC(m, cluster = PetersenCL$firm, order.by = PetersenCL$year)
## these are also the same when observations within each
## cluster are already ordered
vcovPC(m, cluster = ~ firm)
vcovPC(m, cluster = PetersenCL$firm)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.