| ivmodel-package {ivmodel} | R Documentation |
Statistical Inference and Sensitivity Analysis for Instrumental Variables Model
Description
The package fits an instrumental variables (IV) model of the following type. Let Y, D, X, and Z represent the outcome, endogenous variable, p dimensional exogenous covariates, and L dimensional instruments, respectively; note that the intercept can be considered as a vector of ones and a part of the exogenous covariates X.The package assumes the following IV model
Y = X \alpha + D \beta + \epsilon, E(\epsilon | X, Z) = 0
It carries out several IV regressions, diagnostics, and tests associated with the parameter \beta in the IV model. Also, if there is only one instrument, the package runs a sensitivity analysis discussed in Jiang et al. (2015).
The package is robust to most data formats, including factor and character data, and can handle very large IV models efficiently using a sparse QR decomposition.
Details
Supply the outcome Y, the endogenous variable D, and a data frame and/or matrix of instruments Z, and a data frame and/or matrix of exogenous covariates X (optional) and run ivmodel. Alternatively, one can supply a formula. ivmodel will generate all the relevant statistics for the parameter \beta.
The DESCRIPTION file:
| Package: | ivmodel |
| Type: | Package |
| Title: | Statistical Inference and Sensitivity Analysis for Instrumental Variables Model |
| Version: | 1.9.1 |
| Date: | 2023-04-08 |
| Author: | Hyunseung Kang, Yang Jiang, Qingyuan Zhao, and Dylan Small |
| Maintainer: | Hyunseung Kang <hyunseung@stat.wisc.edu> |
| Description: | Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details. |
| Imports: | stats,Matrix,Formula,reshape2,ggplot2 |
| License: | GPL-2 | file LICENSE |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Repository: | CRAN |
| Suggests: | testthat |
Index of help topics:
AR.power Power of the Anderson-Rubin (1949) Test
AR.size Sample Size Calculator for the Power of the
Anderson-Rubin (1949) Test
AR.test Anderson-Rubin (1949) Test
ARsens.power Power of the Anderson-Rubin (1949) Test with
Sensitivity Analysis
ARsens.size Sample Size Calculator for the Power of the
Anderson-Rubin (1949) Test with Sensitivity
Analysis
ARsens.test Sensitivity Analysis for the Anderson-Rubin
(1949) Test
CLR Conditional Likelihood Ratio Test
Fuller Fuller-k Estimator
IVpower Power calculation for IV models
IVsize Calculating minimum sample size for achieving a
certain power
KClass k-Class Estimator
LIML Limited Information Maximum Likelihood Ratio
(LIML) Estimator
TSLS.power Power of TSLS Estimator
TSLS.size Sample Size Calculator for the Power of
Asymptotic T-test
balanceLovePlot Create Love plot of standardized covariate mean
differences
biasLovePlot Create Love plot of treatment bias and
instrument bias
card.data Card (1995) Data
coef.ivmodel Coefficients of the Fitted Model in the
'ivmodel' Object
coefOther Exogenous Coefficients of the Fitted Model in
the 'ivmodel' Object
confint.ivmodel Confidence Intervals for the Fitted Model in
'ivmodel' Object
distributionBalancePlot
Plot randomization distributions of the
Mahalanobis distance
fitted.ivmodel Extract Model Fitted values in the 'ivmodel'
Object
getCovMeanDiffs Get Covariate Mean Differences
getMD Get Mahalanobis Distance
getStandardizedCovMeanDiffs
Get Standardized Covariate Mean Differences
icu.data Pseudo-data based on Branson and Keele (2020)
iv.diagnosis Diagnostics of instrumental variable analysis
ivmodel Fitting Instrumental Variables (IV) Models
ivmodel-package Statistical Inference and Sensitivity Analysis
for Instrumental Variables Model
ivmodelFormula Fitting Instrumental Variables (IV) Models
model.matrix.ivmodel Extract Design Matrix for 'ivmodel' Object
para Parameter Estimation from Ivmodel
permTest.absBias Perform a permutation test using the sum of
absolute biases
permTest.md Perform a permutation test using the
Mahalanobis distance
residuals.ivmodel Residuals from the Fitted Model in the
'ivmodel' Object
vcov.ivmodel Calculate Variance-Covariance Matrix (i.e.
Standard Error) for k-Class Estimators in the
'ivmodel' Object
vcovOther Variance of Exogenous Coefficients of the
Fitted Model in the 'ivmodel' Object
Author(s)
Hyunseung Kang, Yang Jiang, Qingyuan Zhao, and Dylan Small
Maintainer: Hyunseung Kang <hyunseung@stat.wisc.edu>
References
Anderson, T. W. and Rubin, H. (1949). Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics 20, 46-63.
Andrews, D. W. K., Moreira, M. J., and Stock, J. H. (2006). Optimal two-side invariant similar tests for instrumental variables regression. Econometrica 74, 715-752.
Card, D. Using Geographic Variation in College Proximity to Estimate the Return to Schooling. In Aspects of Labor Market Behavior: Essays in Honor of John Vanderkamp, eds. L.N. Christophides, E.K. Grant and R. Swidinsky. 201-222. National Longitudinal Survey of Young Men: https://www.nlsinfo.org/investigator/pages/login.jsp
Fuller, W. (1977). Some properties of a modification of the limited information estimator. Econometrica, 45, 939-953.
Moreira, M. J. (2003). A conditional likelihood ratio test for structural models. Econometrica 71, 1027-1048.
Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica , 393-415.
Wang, X., Jiang, Y., Small, D. and Zhang, N. (2017), Sensitivity analysis and power for instrumental variable studies. Biometrics 74(4), 1150-1160.
Examples
data(card.data)
# One instrument #
Y=card.data[,"lwage"]
D=card.data[,"educ"]
Z=card.data[,"nearc4"]
Xname=c("exper", "expersq", "black", "south", "smsa", "reg661",
"reg662", "reg663", "reg664", "reg665", "reg666", "reg667",
"reg668", "smsa66")
X=card.data[,Xname]
card.model1IV = ivmodel(Y=Y,D=D,Z=Z,X=X)
card.model1IV
# Multiple instruments
Z = card.data[,c("nearc4","nearc2")]
card.model2IV = ivmodel(Y=Y,D=D,Z=Z,X=X)
card.model2IV