ivreg2r
Extended Instrumental Variables Estimation with Diagnostics
Comprehensive instrumental variables and GMM estimation with
automatic diagnostics, inspired by the 'Stata' command 'ivreg2' of Baum,
Schaffer, and Stillman (2003)
ivreg2r 
Instrumental variables and GMM estimation for R with built-in diagnostics, designed to reproduce Stata’s ivreg2 output (Baum, Schaffer & Stillman, 2003, 2007).
Installation
# install.packages("devtools")
devtools::install_github("restatr/ivreg2r")
Example
library(ivreg2r)
data(card)
fit <- ivreg2(lwage ~ exper + expersq + black + smsa + south + smsa66 +
reg662 + reg663 + reg664 + reg665 + reg666 + reg667 +
reg668 + reg669 | educ | nearc2 + nearc4,
data = card, vcov = "robust", small = TRUE)
tidy(fit)
#> # A tibble: 16 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 3.24 0.884 3.66 2.56e- 4 1.50 4.97
#> 2 educ 0.157 0.0526 2.99 2.83e- 3 0.0540 0.260
#> 3 exper 0.119 0.0230 5.18 2.41e- 7 0.0738 0.164
#> 4 expersq -0.00236 0.000368 -6.40 1.83e-10 -0.00308 -0.00163
#> 5 black -0.123 0.0516 -2.39 1.70e- 2 -0.225 -0.0220
#> 6 smsa 0.101 0.0314 3.20 1.37e- 3 0.0391 0.162
#> 7 south -0.143 0.0303 -4.73 2.34e- 6 -0.203 -0.0838
#> 8 smsa66 0.0151 0.0212 0.712 4.77e- 1 -0.0264 0.0566
#> 9 reg662 0.103 0.0379 2.71 6.79e- 3 0.0284 0.177
#> 10 reg663 0.150 0.0371 4.04 5.51e- 5 0.0771 0.223
#> 11 reg664 0.0476 0.0457 1.04 2.98e- 1 -0.0420 0.137
#> 12 reg665 0.154 0.0509 3.03 2.44e- 3 0.0546 0.254
#> 13 reg666 0.173 0.0535 3.23 1.25e- 3 0.0680 0.278
#> 14 reg667 0.142 0.0524 2.71 6.75e- 3 0.0393 0.245
#> 15 reg668 -0.0951 0.0588 -1.62 1.06e- 1 -0.210 0.0201
#> 16 reg669 0.103 0.0427 2.41 1.59e- 2 0.0193 0.187
Formula syntax
Three-part formula: y ~ exogenous | endogenous | excluded_instruments. Each variable appears once; exogenous regressors are automatically included as instruments.
| Part | Contents | Example |
|---|---|---|
| LHS | Dependent variable | lwage |
| 1st RHS | Exogenous regressors | exper + expersq + black + smsa + ... |
| 2nd RHS | Endogenous regressors | educ |
| 3rd RHS | Excluded instruments | nearc2 + nearc4 |
A one-part formula (y ~ x1 + x2) estimates OLS.
Features
Estimators: OLS, 2SLS, LIML, Fuller, k-class, two-step efficient GMM, CUE
Standard errors: classical, robust, one- and two-way clustering, HAC/AC (8 kernels), Kiefer, Driscoll-Kraay, cluster+kernel
Diagnostics: weak identification (Kleibergen-Paap rk, Cragg-Donald, Stock-Yogo critical values), underidentification, overidentification (Sargan, Hansen J), endogeneity, first-stage F/partial R-squared, Anderson-Rubin, Stock-Wright S, orthogonality, redundancy
Integration: tidy(), glance(), augment() via broom
Full output
summary() prints the complete ivreg2 output, including the automatic diagnostics described above, for the model fit in the example:
summary(fit)
#>
#> 2SLS Estimation
#>
#> Call:
#> ivreg2(formula = lwage ~ exper + expersq + black + smsa + south +
#> smsa66 + reg662 + reg663 + reg664 + reg665 + reg666 + reg667 +
#> reg668 + reg669 | educ | nearc2 + nearc4, data = card, vcov = "robust",
#> small = TRUE)
#>
#> Observations: 3,010
#> VCV type: Robust, small-sample corrected
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.2367114 0.8842788 3.660 0.000256 ***
#> educ 0.1570593 0.0525525 2.989 0.002825 **
#> exper 0.1188149 0.0229516 5.177 2.41e-07 ***
#> expersq -0.0023565 0.0003684 -6.397 1.83e-10 ***
#> black -0.1232778 0.0516278 -2.388 0.017010 *
#> smsa 0.1007530 0.0314458 3.204 0.001369 **
#> south -0.1431945 0.0302678 -4.731 2.34e-06 ***
#> smsa66 0.0150626 0.0211683 0.712 0.476792
#> reg662 0.1027473 0.0379324 2.709 0.006793 **
#> reg663 0.1499316 0.0371230 4.039 5.51e-05 ***
#> reg664 0.0475676 0.0456953 1.041 0.297972
#> reg665 0.1544801 0.0509365 3.033 0.002444 **
#> reg666 0.1729728 0.0535430 3.231 0.001249 **
#> reg667 0.1420355 0.0523988 2.711 0.006753 **
#> reg668 -0.0950611 0.0587578 -1.618 0.105801
#> reg669 0.1029760 0.0426891 2.412 0.015915 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> R-squared: 0.1702
#> Adj. R-squared: 0.1660
#> F(15, 2994): 51.4 (p < 2.2e-16)
#> Root MSE: 0.4053
#>
#> Underidentification test (Kleibergen-Paap rk LM statistic):
#> Chi-sq(2) = 16.37 (p = 0.0003)
#>
#> Weak identification test:
#> Cragg-Donald Wald F: 7.89
#> Kleibergen-Paap rk Wald F: 8.32
#> (Stock-Yogo critical values are for iid errors)
#> Stock-Yogo critical values (IV size):
#> 10% maximal IV size 19.93
#> 15% maximal IV size 11.59
#> 20% maximal IV size 8.75
#> 25% maximal IV size 7.25
#>
#> Overidentification test (Hansen J):
#> Chi-sq(1) = 1.27 (p = 0.2600)
#>
#> Weak-instrument-robust inference:
#> H0: B1=0 and orthogonality conditions are valid
#> Anderson-Rubin Wald F(2,2993) = 5.28 (p = 0.0051)
#> Anderson-Rubin Wald Chi-sq(2) = 10.63 (p = 0.0049)
#> Stock-Wright LM S Chi-sq(2) = 10.61 (p = 0.0050)
#>
#> Endogeneity test:
#> Chi-sq(1) = 2.83 (p = 0.0925)
#> Tested: educ
#>
#> First-stage diagnostics:
#> Endogenous F-stat p-value Partial R2 Shea PR2 SW F AP F
#> educ 8.32 0.0002 0.0052 0.0052 8.32 8.32
#>
#> Instrumented: educ
#> Included instruments: exper, expersq, black, smsa, south, smsa66, reg662, reg663, reg664, reg665, reg666, reg667, reg668, reg669
#> Excluded instruments: nearc2, nearc4
Validation
Every estimator, variance estimator, and diagnostic is tested against output generated by Stata’s ivreg2 itself. Checked-in Stata scripts run each specification and write the results to machine-readable fixture files; the R test suite then reproduces them at tight, centrally documented tolerances. See How ivreg2r is validated against Stata for more details.
Stata migration
Common translations (see vignette("introduction") for the full table and intentional differences):
| Stata | R (ivreg2r) |
|---|---|
ivreg2 y x (endo = z1 z2) |
ivreg2(y ~ x \| endo \| z1 + z2, data = d) |
ivreg2 ..., robust |
ivreg2(..., vcov = "robust") |
ivreg2 ..., robust small |
ivreg2(..., vcov = "robust", small = TRUE) |
ivreg2 ..., cluster(g) |
ivreg2(..., clusters = ~ g) |
ivreg2 ..., cluster(g) small |
ivreg2(..., clusters = ~ g, small = TRUE) |
ivreg2 ..., liml |
ivreg2(..., method = "liml") |
ivreg2 ..., gmm2s |
ivreg2(..., method = "gmm2s") |
Learn more
vignette("introduction")— OLS, 2SLS, diagnostics, robust/cluster SEs, weights, broom integration, Stata migration referencevignette("advanced-iv")— LIML, Fuller, k-class, COVIV, two-way clustering, reduced-form, model comparisonvignette("time-series-gmm")— HAC/AC, Kiefer, Driscoll-Kraay, GMM, CUE, partialling, user matrices
Citation
citation("ivreg2r")
DiTraglia F (2026). _ivreg2r: Extended Instrumental Variables
Estimation with Diagnostics_. R package,
<https://github.com/restatr/ivreg2r>.
The Stata ivreg2 command that inspired this package is described in:
Baum C, Schaffer M, Stillman S (2003). "Instrumental Variables and
GMM: Estimation and Testing." _The Stata Journal_, *3*(1), 1-31.
doi:10.1177/1536867X0300300101
<https://doi.org/10.1177/1536867X0300300101>.
Baum C, Schaffer M, Stillman S (2007). "Enhanced Routines for
Instrumental Variables/Generalized Method of Moments Estimation and
Testing." _The Stata Journal_, *7*(4), 465-506.
doi:10.1177/1536867X0800700402
<https://doi.org/10.1177/1536867X0800700402>.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.
Development
ivreg2r is part of restatr, a project that uses LLM assistance to port user-contributed Stata modules from the SSC Archive to R and Python. The package was developed with Claude Code, working from the original GPL-3-licensed ivreg2 Stata source code, created by Baum, Schaffer, and Stillman. Every package feature has been numerically validated against the Stata original. For more details see How ivreg2r is validated against Stata.
License
GPL-3. Adapted from the Stata package ivreg2 by Christopher F Baum, Mark E Schaffer, and Steven Stillman, also distributed under GPL-3.