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The instrument cannot be correlated with the error term in the explanatory equation, conditionally on the other covariates. In other words, the instrument cannot suffer from the same problem as the original predicting variable.
Should instrumental variables be used as matching variables?
The result for matching is a special case of a more general result: including in a regression analysis any functions of instrumental variables, along with an endogenous explanatory variable and other covariates, leads to more asymptotic bias than excluding the instrumental variables.
Are instrumental variables exogenous?
Finding Instrumental Variables Exogenous —not affected by other variables in the system (i.e. Cov(z,ε) = 0). Correlated with X, an endogenous explanatory variable (i.e. Cov(Z,X) ≠ 0).
What are instrumental variables used for?
Instrumental variables (IVs) are used to control for confounding and measurement error in observational studies. They allow for the possibility of making causal inferences with observational data. Like propensity scores, IVs can adjust for both observed and unobserved confounding effects.
Can you use two instrumental variables at the same time?
Can you use two instrumental variables z 1 and z 2 at the same time for x 1 and x 2, in the following regression model e is the error term. If you have endogeneity between a dependent variable and error term the use of Instrument variables are the way to go.
When to use an instrumental variable in regression?
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: 1. Omitted variable bias from a variable that is correlated with X but is unobserved, so cannot be included in the regression. 2.
When to use an endogenous regressor in econometrics?
The endogenous regressor linear model, a workhorse of econometric applications, assumes that the dependent variable and regressors are both random and satisfy the linear relation