When you have the right mental model, this is an appropriate approach.
Not every mental model is correct, either locally to a domain or globally in all cases.
1 < 2, in almost all cases except for small Z_{0,1} or similar type cases where mod functions modify the space to something exotic or comparator functions are defined far out of the typical. If you think there is a case for 2 < 1, and you aren't appealing to something exotic, you have the wrong approach.
Any mental model that assigns zero weight to the probability of being wrong is a wrong mental model.
That said, biases arising from endogeneity might have negative effects too. You can't conclude a parameter should have a different/zero sign just because of endogeneity, you have to go fix your model, and re-estimate the parameters.
tomrod|3 years ago
Not every mental model is correct, either locally to a domain or globally in all cases.
1 < 2, in almost all cases except for small Z_{0,1} or similar type cases where mod functions modify the space to something exotic or comparator functions are defined far out of the typical. If you think there is a case for 2 < 1, and you aren't appealing to something exotic, you have the wrong approach.
_v7gu|3 years ago
That said, biases arising from endogeneity might have negative effects too. You can't conclude a parameter should have a different/zero sign just because of endogeneity, you have to go fix your model, and re-estimate the parameters.