Is reverse causality and endogeneity problem?
We have the problem of endogeneity for 3 reasons: — 1) omitted variable bias (a relevant X is omitted), — 2) reverse causality (X affects Y but Y also affects X), — 3) measurement error (we cannot measure variables accurately).
What is reverse causality endogeneity?
Reverse causality means that X and Y are associated, but not in the way you would expect. Instead of X causing a change in Y, it is really the other way around: Y is causing changes in X. In epidemiology, it’s when the exposure-disease process is reversed; In other words, the exposure causes the risk factor.
Which is worse endogeneity or reverse causality?
Endogeneity is a particularly tough issue to address and even the most hardcore econometric modelers will admit that using IVs introduces as many problems as it solves, i.e., the cure can be worse than the disease. Among the recommended “solutions” is 2SLS.
Why is endogeneity rarely a reality in applied work?
This is rarely the reality in applied work and is certainly not the case with your information. Given that endogeneity is mostly a theoretical problem, the analyst is almost forced to use theory to drive a solution. Empirically, this makes it an almost insoluble, indeterminate problem.
What’s the problem with the book reverse causality?
My problem with his book is that it’s from a theoretician’s perspective, i.e., his examples are based on “toy” data where the input matrices are neat, balanced and completely observed, i.e., he doesn’t have to deal with missing values, unbalanced matrices wrt time and sample size, censoring or any other irregularities.
How is reverse causality related to lead lag structures?
You mentioned “reverse causality.” To me, that suggests an interest in determining lead-lag structures without an a priori theory about the direction of the causal structure. This paper by Sornette suggests a nonparametric approach to understanding lead-lag structures in which time’s arrow is treated agnostically.