clusterSEs (version 2.2)

cluster.bs.ivreg: Pairs Cluster Bootstrapped p-Values For Regression With Instrumental Variables

Description

This software estimates p-values using pairs cluster bootstrapped t-statistics for instrumental variables regression 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.ivreg(mod, dat, cluster, ci.level = 0.95, boot.reps = 1000,
  stratify = FALSE, cluster.se = TRUE, report = TRUE, prog.bar = TRUE)

Arguments

mod

A model estimated using ivreg.

dat

The data set used to estimate mod.

cluster

A formula of the clustering variable.

ci.level

What confidence level should CIs reflect?

boot.reps

The number of bootstrap samples to draw.

stratify

Sample clusters only (= FALSE) or clusters and observations by cluster (= TRUE).

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).

Value

• A list with the elements
• p.values
  A matrix of the estimated p-values.
• ci
  A matrix of confidence intervals.

References

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.

Examples

# example one: predict cigarette consumption
data("CigarettesSW", package = "AER") 
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)
fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | 
    log(rincome) + tdiff + I(tax/cpi), data = CigarettesSW)

# compute pairs cluster bootstrapped p-values
cluster.bs.c <- cluster.bs.ivreg(fm, dat = CigarettesSW, cluster = ~state, report = T)



# example two: pooled IV analysis of employment
require(plm)
require(AER)
data(EmplUK)
EmplUK$lag.wage <- lag(EmplUK$wage)
emp.iv <- ivreg(emp ~ wage + log(capital+1) | output + lag.wage + log(capital+1), data = EmplUK)

# compute cluster-adjusted p-values
cluster.bs.e <- cluster.bs.ivreg(mod = emp.iv, dat = EmplUK, cluster = ~firm)