Instrumental variables can help address endogeneity issues in regression analysis from "summary" of Introduction to Econometrics by Christopher Dougherty
Instrumental variables are a powerful tool that can be used to tackle the problem of endogeneity in regression analysis. Endogeneity arises when an independent variable is correlated with the error term in a regression model, leading to biased and inconsistent estimates of the coefficients. This can happen when there are omitted variables, measurement error, or simultaneity issues present in the data. By finding a variable that is correlated with the endogenous variable but uncorrelated with the error term, instrumental variables provide a way to overcome endogeneity. Essentially, instrumental variables act as a proxy for the endogenous variable, allowing us to estimate the causal effect of the variable of interest on the dependent variable more accurately. The key requirement for instrumental variables to work effectively is that they must satisfy two conditions: relevance and exogeneity. Relevance means that the instrumental variable must be correlated with the endogenous variable, while exogeneity requires that the instrumental variable is uncorrelated with the error term in the regression model. When these conditions are met, instrumental variables can help us obtain consistent and unbiased estimates of the coefficients in the regression model. This is because instrumental variables provide a way to isolate the variation in the endogenous variable that is unrelated to the error term, allowing us to identify the true causal relationship between the variables of interest.- Instrumental variables are a valuable technique for addressing endogeneity issues in regression analysis, providing a robust way to estimate causal relationships in the presence of correlated errors. By carefully selecting and validating instrumental variables, researchers can improve the validity and reliability of their regression models, leading to more accurate and informative results.
