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REndo: Internal Instrumental Variables to Address Endogeneity


Gui, Raluca Ioana; Meierer, Markus; Schilter, Patrik; Algesheimer, René (2023). REndo: Internal Instrumental Variables to Address Endogeneity. Journal of Statistical Software, 107(3):1-43.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:370 Education
Uncontrolled Keywords:Statistics, Probability and Uncertainty, Statistics and Probability, Software
Scope:Discipline-based scholarship (basic research)
Language:English
Date:9 September 2023
Deposited On:18 Sep 2023 07:13
Last Modified:29 Jun 2024 01:38
Publisher:Foundation for Open Access Statistics
ISSN:1548-7660
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.18637/jss.v107.i03
Other Identification Number:merlin-id:24059
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 3.0 Unported (CC BY 3.0)