Endogeneity: An inconvenient truth (for researchers), by John Antonakis

It is well known that endogeneity leads to inconsistent estimates. Unfortunately, many researchers working outside of economics are not aware of the problem of endogeneity and how to deal with it. Prof. John Antonakis shows how the two-stage least squares (2SLS) estimator recovers causal estimates in the presence of endogeneity (which includes the problem of common-method variance). He also shows that endogeneity can even be prevalent in experimental designs, when researchers estimate mediation models; that is, where the causal effect of an exogenous variable on a dependent variable is mediated by an endogenous variable (or a manipulation check).

[@elielias6006]

Thank you for the wonderful video

[@jamansa]

Structural equation modelling allows the inclusion and testing correlation errors. Why don't you like SEM? Evenmore, you get a goodness of fit of the whole model on top of the paremeter estimates.
Which is the impact of choosing a wrong-enough "insrtument" variable? Choosing different variables may result in different errors "u", not necessarily all of them uncorrelated with "e".
Too much marketing to "gain" for people with low statistical knowledge...
But indeed, since I used 2SLS my wife loves me more, my bank account increased, all my papers were published, my computer did not get virus anymore... Yes, I can say that 2SLS saved my live ... :(

Nice video!