cluster.bs.plm: Pairs Cluster Bootstrapped p-Values For PLM

In clusterSEs: Calculate Cluster-Robust p-Values and Confidence Intervals

Description

This software estimates p-values using pairs cluster bootstrapped t-statistics for fixed effects panel linear models (Cameron, Gelbach, and Miller 2008). The data set is repeatedly re-sampled by cluster, a model is estimated, and inference is based on the sampling distribution of the pivotal (t) statistic.

Usage

cluster.bs.plm(
  mod,
  dat,
  cluster = "group",
  ci.level = 0.95,
  boot.reps = 1000,
  cluster.se = TRUE,
  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?
boot.reps	The number of bootstrap samples to draw.
cluster.se	Use clustered standard errors (= TRUE) or ordinary SEs (= FALSE) for bootstrap replicates.
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.

Author(s)

Justin Esarey

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.bs.plm(mod=emp.1, dat=EmplUK, cluster="group", ci.level = 0.95, 
          boot.reps = 1000, cluster.se = TRUE, report = TRUE, 
          prog.bar = TRUE)

# cluster on time

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


## End(Not run)

clusterSEs documentation built on April 6, 2021, 1:06 a.m.