clusterSEs (version 2.2)

cluster.bs.mlogit: Pairs Cluster Bootstrapped p-Values For mlogit

Description

This software estimates p-values using pairs cluster bootstrapped t-statistics for multinomial logit 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.mlogit(mod, dat, cluster, ci.level = 0.95, boot.reps = 1000,
  cluster.se = TRUE, report = TRUE, prog.bar = TRUE, unique.id = TRUE)

Arguments

mod

A model estimated using mlogit.

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.

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).

unique.id

Should id (from mlogit.data) be made unique for bootstrap replicates (= TRUE) or repeated across replicates (= 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: train ticket selection
# see http://cran.r-project.org/web/packages/mlogit/vignettes/mlogit.pdf
require(mlogit)
data("Train", package="mlogit")
Train$ch.id <- paste(Train$id, Train$choiceid, sep=".")
Tr <- mlogit.data(Train, shape = "wide", choice = "choice", varying = 4:11,
                  sep = "", alt.levels = c(1, 2), id = "id")
Tr$price <- Tr$price/100 * 2.20371
Tr$time <- Tr$time/60
ml.Train <- mlogit(choice ~ price + time + change + comfort | -1, Tr)

# compute pairs cluster bootstrapped p-values
# note: few reps to speed up example
cluster.bs.tr <- cluster.bs.mlogit(ml.Train, Tr, ~ id, boot.reps=100)



# example two: predict type of heating system installed in house
# note: few reps to speed up example
require(mlogit)
data("Heating", package = "mlogit")
H <- Heating
H.ml <- mlogit.data(H, shape="wide", choice="depvar", varying=c(3:12))
m <- mlogit(depvar~ic+oc, H.ml)

# compute pairs cluster bootstrapped p-values
cluster.bs.h <- cluster.bs.mlogit(m, H.ml, ~ region, boot.reps=100)