R Package REndo, developed by the URPP Social Networks team, has passed 50,000 downloads on CRAN since its first release.
This month, REndo, a tool for fitting linear models with endogenous regressors using latent instrumental variables, surpasses 50,000 downloads on CRAN since its initial release in 2016.
The popular R package developed by Raluca Gui, Markus Meierer, Rene Algesheimer, and Patrik Schilter was recently featured in the Journal of Statistical Software in an article titled "REndo: Internal Instrumental Variables to Address Endogeneity".
Endogeneity arises when the independence assumption between an explanatory variable and the error in a statistical model is violated. Among its most common causes are omitted variable bias (e.g., ability in the returns to education estimation), measurement error (e.g., survey response bias), or simultaneity (e.g., advertising and sales).
Instrumental variable estimation is a common treatment when endogeneity is of concern. However valid, strong external instruments are difficult to find. Consequently, statistical methods for correcting endogeneity without external instruments have been advanced. They are called internal instrumental variable models (IIV).
REndo implements the following instrument-free methods:
Latent instrumental variables approach (Ebbes, Wedel, Boeckenholt, and Steerneman 2005)
Higher moments estimation (Lewbel 1997)
Heteroskedastic error approach (Lewbel 2012)
Joint estimation using copula (Park and Gupta 2012)
Multilevel GMM (Kim and Frees 2007)