New publication alert! The article "REndo: Internal Instrumental Variables to Address Endogeneity" is now available for download.
We congratulate the research team comprising Raluca Gui (University of Zurich), Markus Meierer (University of Geneva), Patrik Schilter (University of Zurich), and René Algesheimer (University of Zurich) for the publication of the articleREndo: Internal Instrumental Variables to Address Endogeneity in the Journal of Statistical Software
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.
Gui, R., Meierer, M., Schilter, P., & Algesheimer, R. (2023). REndo: Internal Instrumental Variables to Address Endogeneity. Journal of Statistical Software, 107(3), 1–43. https://doi.org/10.18637/jss.v107.i03