cluster.bs.ivreg {clusterSEs}

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,
  output.replicates = FALSE,
  seed = NULL
)

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).

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.

replicates

Optional: A matrix of the coefficient estimates from each cluster bootstrap replicate.

Note

Code to estimate clustered standard errors by Mahmood Arai: http://thetarzan.wordpress.com/2011/06/11/clustered-standard-errors-in-r/. Cluster SE degrees of freedom correction = (M/(M-1)) with M = the number of clusters.

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: 

##############################################
# example one: predict cigarette consumption
##############################################
require(AER)
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)


## End(Not run)