ivmodel-package: Statistical Inference and Sensitivity Analysis for...

In ivmodel: Statistical Inference and Sensitivity Analysis for Instrumental Variables Model

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: This package was not yet installed at build time.
Index: This package was not yet installed at build time.

Author(s)

Hyunseung Kang, Yang Jiang, Qingyuan Zhao, and Dylan Small

Maintainer: Hyunseung Kang <[e-mail]>

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

ivmodel documentation built on April 9, 2023, 5:08 p.m.