clusterSEs (version 2.6.2)

cluster.wild.plm: Wild Cluster Bootstrapped p-Values For PLM

Description

This software estimates p-values using wild cluster bootstrapped t-statistics for fixed effects panel linear models (Cameron, Gelbach, and Miller 2008). Residuals are repeatedly re-sampled by cluster to form a pseudo-dependent variable, a model is estimated for each re-sampled data set, and inference is based on the sampling distribution of the pivotal (t) statistic. The null is never imposed for PLM models.

Usage

cluster.wild.plm(mod, dat, cluster, ci.level = 0.95, boot.reps = 1000,
  report = TRUE, prog.bar = TRUE, output.replicates = FALSE,
  seed = NULL)

Arguments

mod

A "within" model estimated using plm.

dat

The data set used to estimate mod.

cluster

Clustering dimension ("group", the default, or "time").

ci.level

What confidence level should CIs reflect? (Note: only reported when impose.null == FALSE).

boot.reps

The number of bootstrap samples to draw.

report

Should a table of results be printed to the console?

prog.bar

Show a progress bar of the bootstrap (= TRUE) or not (= FALSE).

output.replicates

Should the cluster bootstrap coefficient replicates be output (= TRUE) or not (= FALSE)?

seed

Random number seed for replicability (default is NULL).

Value

A list with the elements

p.values

A matrix of the estimated p-values.

ci

A matrix of confidence intervals (if null not imposed).

References

Esarey, Justin, and Andrew Menger. 2017. "Practical and Effective Approaches to Dealing with Clustered Data." Political Science Research and Methods forthcoming: 1-35. <URL:http://jee3.web.rice.edu/cluster-paper.pdf>.

Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller. 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors." The Review of Economics and Statistics 90(3): 414-427. <DOI:10.1162/rest.90.3.414>.

Examples

# NOT RUN {
# predict employment levels, cluster on group
require(plm)
data(EmplUK)

emp.1 <- plm(emp ~ wage + log(capital+1), data = EmplUK, model = "within",
             index=c("firm", "year"))
cluster.wild.plm(mod=emp.1, dat=EmplUK, cluster="group", ci.level = 0.95,
        boot.reps = 1000, report = TRUE, prog.bar = TRUE)

# cluster on time
cluster.wild.plm(mod=emp.1, dat=EmplUK, cluster="time", ci.level = 0.95, 
            boot.reps = 1000, report = TRUE, prog.bar = TRUE)

# }