What problems can instrumental variables address Why are the

What problems can instrumental variables address? Why are they problems? Why (intuitvely) can IV address them? Why not always use IV?

Solution

Instrumental Variables (IV) estimation is used when the model has endogenous X’s.

IV can thus be used to address the following important threats to internal validity:

• Omitted variable bias from a variable that is correlated with _ but is unobserved, so cannot be included in the regression;

• Simultaneous causality bias (endogenous explanatory variables; X causes Y, Y causes X);

• Errors-in-variables bias (X is measured with error)

Instrumental variables regression can eliminate bias from these three sources.

An instrumental variable, Z is uncorrelated with the disturbance € but is correlated with X (e.g., proximity to college might be correlated with schooling but not with wage residuals)

• With this new variable, the IV estimator should capture only the effects on Y of shifts in X induced by whereas the OLS estimator captures not only the direct effect of on [1] but also the effect of the included measurement error and/or endogeneity

• IV is not as efficient as OLS (especially if Z only weakly correlated with X, i.e. when we have so-called “weak instruments”) and only has large sample properties (consistency).