Dear Stata Users,

I am trying to estimate a model which relates the presence (1) or absence (0) of a policy in a given jurisdiction (i) and year (t) to a set of covariates describing the structure of the economy (e.g. VA of industry as a % of total GDP, electricity production mix, GDP,...). I am currently working with -xtlogit, re- and -xtprobit- on a dataset that has N=118 (national jurisdictions) and T=26 (years).

However, I am worried that in doing so, a reverse causality problem may arise in the sense that such policies (at least the most stringent) may also affect some of the covariates, if not contemporaneously, at least with a lag.

I am currently unsure of the following:
  1. Does this type of endogeneity plague coefficient estimates with bias the same way it does for standard linear models?
  2. If yes, what would be the appropriate way to handle it given that I don't have access to (external) instrumental variables? [My current model relates lagged values of the covariates to the current value of the binary outcome variable, which I believe breaks the contemporaneous correlation between the covariate and the error term but not the dynamic one.]
I would very much appreciate any help on this problem.

Many thanks,
Geoffroy