vcovPC: Panel-Corrected Covariance Matrix Estimation

Description Usage Arguments Details Value References See Also Examples

View source: R/vcovPC.R

Description

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

Usage

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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, a list (or data.frame) thereof, or a formula specifying which variables from the fitted model should be used (see examples). By default (cluster = NULL), either attr(x, "cluster") is used (if any) or otherwise every observation is assumed to be its own cluster.

order.by

a variable, list/data.frame, or formula indicating the aggregation within time periods. By default attr(x, "order.by") is used (if any) or otherwise observations within clusters are assumed to be ordered.

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.

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.

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).

Value

A matrix containing the covariance matrix estimate.

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. 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. http://www.jstor.org/stable/2082979

Berger S, Graham N, Zeileis A (2017). “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Technical Report 2017-12, Working Papers in Economics and Statistics, Research Platform Empirical and Experimental Economics, Universitaet Innsbruck. http://EconPapers.RePEc.org/RePEc:inn:wpaper:2017-12.

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

See Also

vcovCL, vcovPL

Examples

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## 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)

sandwich documentation built on May 1, 2019, 8:42 p.m.