Endogeneity assessment and control

Instrumental variables can be used in WarpPLS, starting in version 6.0, to test and control for endogeneity, which occurs when the structural error term for an endogenous variable is correlated with any of the variable’s predictors. For example, let us consider a simple population model with the following links A > B and B > C. This model presents endogeneity with respect to C, because variation flows from A to C via B, leading to a biased estimation of the path for the link B > C via ordinary least squares regression. Adding a link from A to C could be argued as “solving the problem”, but in fact it creates the possibility of a type I error, since the link A > C does not exist at the population level. A more desirable solution to this problem is to create an instrumental variable iC, incorporating only the variation of A that ends up in C and nothing else, and revise the model so that it has the following links: A > B, B > C and iC > C. The link iC > C can be used to test for endogeneity, via its P value and effect size. This link (i.e., iC > C) can also be used to control for endogeneity, thus removing the bias when the path coefficient for the link B > C is estimated via ordinary least squares regression. To create instrumental variables to test and control for endogeneity you should use the sub-option “Single stochastic variation sharing”, under the new menu option “Explore analytic composites and instrumental variables”.

Related YouTube video:

Test and Control for Endogeneity in WarpPLS

http://youtu.be/qCvvUxR978U