REndo: Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables
Version 1.2

Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) higher moments approach as well as Lewbel's (2012) heteroskedasticity approach, Park and Gupta's (2012) joint estimation method that uses Gaussian copula and Kim and Frees's (2007) multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. This version: - solves an error occurring when using the multilevelIV() function with two levels, random intercept. - returns the AIC and BIC for copulaCorrection() (method 1) and latentIV() methods. - residuals and fitted values can be saved by users for latentIV() and copulaCorrection() methods. - improves the summary methods for copulaCorrection() and multilevelIV() functions.

Getting started

Package details

AuthorRaluca Gui, Markus Meierer, Rene Algesheimer
Date of publication2017-04-10 14:33:46 UTC
MaintainerRaluca Gui <>
Package repositoryView on CRAN
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REndo documentation built on May 29, 2017, 2:54 p.m.