Statistical Models and Causal Inference: Endogeneity in Probit Response Models

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Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference* (2014, background): “Additionally, these models suffer from difficulty in maximizing the likelihood functions and trouble with calculating appropriate standard errors [119].”
Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity (2011, methodology): “The additional drawback of VGAM is that it suffers from convergence problems as explained and illustrated by Freedman & Sekhon (2010) in a simpler context.”
Governmental venture capital in Europe: Screening and certification (2016, methodology): “…GVC-PVC co-investments from the analysis on the probabilities on receiving PVC in order to avoid overestimating the certification effect of GVC. 16 Freedman and Sekhon (2010) indicate that the use of Heckman (1979) two-step method when the second stage is a probit model may create some biases in…”
A Practical Comparison of the Bivariate Probit and Linear IV Estimators (2011, background): “Also as noted by Freedman and Sekhon (2010), part of the diffi culty may be caused by numerical issues with Stata’s implementation of likelihood maximization, as often the likelihood function is very flat and the algorithm fails to find the global maximum. repeated many times, and the size-α…”