Clustered Jackknife Instrumental Variables Estimation.

clusterIV

Clustered instrumental variables estimation and inference for R.

clusterIV is a home for instrumental-variables methods that stay valid under clustered errors with many instruments. The current release provides cjive(), the cluster-jackknife IV estimator (CJIVE) of Frandsen, Leslie and McIntyre (2025) for a single endogenous regressor in a just-identified design — the judge/examiner and shift-share settings where the instrument is many and the errors are clustered. Each observation's first-stage value is fitted from a regression that leaves out the observation's entire cluster, which annihilates the within-cluster dependence that otherwise reintroduces the many-instrument bias of two-stage least squares. The leave-cluster-out fits are computed by an exact Woodbury block update — one Cholesky of the instrument Gram matrix plus a small solve per cluster, never G refactorisations — so the estimator runs comfortably on samples in the hundreds of thousands. Base R only (Imports: stats).

Installation

# install.packages("remotes")
remotes::install_github("atal-kat/Clustered-Estimation-and-Inference")

Usage

Formula interface, y ~ x | z (the bar separates the endogenous regressor from the instruments); a factor on the instrument side is a judge design:

library(clusterIV)
fit <- cjive(wage ~ incarcerated | judge_id, data = cases, cluster = ~courtroom)
summary(fit)

Matrix/vector interface, with a numeric instrument matrix:

cjive(y, x, z, cluster = g, controls = X, weights = w)

controls are partialled out by Frisch-Waugh-Lovell (fixed effects allowed), weights are optional precision weights, and confint(), coef(), vcov() work as usual.

Comparing estimators

iv_compare() reproduces the shape of FLM's Table 1: OLS, 2SLS, JIVE and CJIVE on the same cluster-robust IV sandwich standard error, only the constructed instrument differing between rows.

iv_compare(y, x, z, cluster = g)
#>   estimator coefficient     se statistic  p.value conf.low conf.high
#> 1       OLS         ...    ...       ...      ...      ...       ...
#> 2      2SLS         ...    ...       ...      ...      ...       ...
#> 3      JIVE         ...    ...       ...      ...      ...       ...
#> 4     CJIVE         ...    ...       ...      ...      ...       ...

The covariate/intercept convention

There is one rule: the dense Frisch-Waugh-Lovell route is the default everywhere. Covariates (and the intercept) are partialled out globally, then the leave-cluster-out fit runs on the residuals. A grouping-factor z is expanded to a dummy design (one reference level dropped, the intercept supplying the rest) and run through the same path, so cjive() and iv_compare() return the identical CJIVE for any design.

FLM's printed closed form for the pure judge design — the leave-cluster-out group mean of x — is available on explicit request via method = "leaveout_mean" (grouping-factor z, intercept-only controls). It differs from the default by an O(1/n_g) intercept term and is never selected automatically.

Reference

Frandsen, B., Leslie, E. & McIntyre, S. (2025). Cluster Jackknife Instrumental Variables Estimation. Review of Economics and Statistics.