Endogeneity is a key challenge when aiming to uncover causal relationships in empirical research. Reasons are manifold, i.e. omitted variables, measurement error or simultaneity. These might lead to the unwanted correlation between the independent variables and the error term of a statistical model. While external instrumental variables methods can be used to control for endogeneity, these approaches require additional information which is usually difficult to obtain. Internal instrumental variable (IIV) methods address this issue by treating endogeneity without the need of additional variables, taking advantage of the structure of the data. Implementations of IIV are rare. Thereby, this project proposes the R package “REndo” that implements five instrument-free methods: the latent instrumental variables approach (Ebbes et al. 2005), the higher moments estimation (Lewbel 1997), the heteroskedastic error approach (Lewbel 2012), the joint estimation using copula (Park and Gupta 2012) and the multilevel GMM (Kim and Frees 2007). The package can already be downloaded from CRAN.

Authors: Raluca Gui, Markus Meierer and René Algesheimer