Instrumental variable exclusion restriction When verifying whether a potential instrumental variable is valid, how should I check if it meets the exclusion restriction? To reject that it meets the exclusion restriction, do I simply have to come up with a excluded variable that affects the dependent variable? Or does the excluded variable have to be an omitted variable, namely that it is correlated with the independent and dependent variables of the model? 
 A: There are two criteria for good instruments:


*

*The instrument $z$ is correlated with the endogenous variable $x$ (relevance).

*The instrument $z$ affects dependent variable $y$ only through $x$. In other words, $z$ itself does not cause $y$. This is the exclusion restriction.


You can check 1 (relevance) statistically, but must make good arguments to support 2 (exclusion).
For example, suppose we want to estimate the effect of police ($x$) on crime ($y$) in a cross-section of cities. One issue is that places with lots of crime will hire more police. We therefore seek an instrument $z$ that is correlated with the size of the police force, but unrelated to crime.
One possible $z$ is number of firefighters. The assumptions are that cities with lots of firefighters also have large police forces (relevance) and that firefighters do not affect crime (exclusion). Relevance can be checked with the reduced form regressions, but whether firefighters also affect crime is something to be argued for. Theoretically, they do not and are therefore a valid instrument.
If you are curious about this specific example, see


*

*Levitt (1997)

*McCrary (2002)

*Levitt (2002)
