REndo: Internal Instrumental Variables to Address Endogeneity
Dublin Core
Title
REndo: Internal Instrumental Variables to Address Endogeneity
Subject
endogeneity, internal instrumental variables, multilevel models.
Description
Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is
violated. The causes of endogeneity are manifold and include response bias in surveys,
omission of important explanatory variables, or simultaneity between explanatory and
response variables. Instrumental variable estimation provides a possible solution. However, valid and strong external instruments are difficult to find. Consequently, internal
instrumental variable approaches have been proposed to correct for endogeneity without relying on external instruments. The R package REndo implements various internal
instrumental variable approaches, i.e., latent instrumental variables estimation (Ebbes,
Wedel, Boeckenholt, and Steerneman 2005), higher moments estimation (Lewbel 1997),
heteroscedastic error estimation (Lewbel 2012), joint estimation using copula (Park and
Gupta 2012) and multilevel generalized method of moments estimation (Kim and Frees
2007). Package usage is illustrated on simulated and real-world data.
violated. The causes of endogeneity are manifold and include response bias in surveys,
omission of important explanatory variables, or simultaneity between explanatory and
response variables. Instrumental variable estimation provides a possible solution. However, valid and strong external instruments are difficult to find. Consequently, internal
instrumental variable approaches have been proposed to correct for endogeneity without relying on external instruments. The R package REndo implements various internal
instrumental variable approaches, i.e., latent instrumental variables estimation (Ebbes,
Wedel, Boeckenholt, and Steerneman 2005), higher moments estimation (Lewbel 1997),
heteroscedastic error estimation (Lewbel 2012), joint estimation using copula (Park and
Gupta 2012) and multilevel generalized method of moments estimation (Kim and Frees
2007). Package usage is illustrated on simulated and real-world data.
Creator
Raluca Gui
Source
https://www.jstatsoft.org/article/view/v107i03
Publisher
University of Zurich
Date
September 2023
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Raluca Gui, “REndo: Internal Instrumental Variables to Address Endogeneity,” Repository Horizon University Indonesia, accessed May 11, 2025, https://repository.horizon.ac.id/items/show/8306.