Instrumental Variables Method (IV) | Regression Analysis and Estimation Methods | Stata Topic 44 

Stata Tutorials Topic 44: Instrumental Variables Method (IV) | Regression Analysis and Estimation Methods Using Stata
Hi, I am Bob. Economists often try to find the causal effects of an explanatory variable on the outcome variable. For example, people are very interested in the return to education. We want to know whether education has a causal effect on wages. We often use available observed data to establish a causal relationship between hourly wage and educational attainment. The challenge of using observed data in our research is whether the estimates are unbiased or consistent estimates for the true parameter. In this OLS model, we regress wage on education, experience, race, and region variables. We have no data on the worker's ability and personality. The unobserved factors, such as the worker's ability and personality, are called omitted variables and become elements of the error term. The worker's ability and personality are likely to affect the wage they earn and also relate to the worker's educational attainment. If this is the case, the OLS estimate of the return to education will be biased. In other words, the estimate is not expected to equal the true parameter beta1.
#IV #OLS #InstrumentalVariable #OmittedVariableBias #RegressionAnalysisandEstimationMethods #endogenous
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