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Unformatted text preview: ECON 103, Lecture 15A: Instrumental Variables I Maria Casanova May 26th (version 0) Maria Casanova Lecture 15A Requirements for this lecture: Chapter 12 of Stock and Watson Maria Casanova Lecture 15A 0. Introduction In lecture 12 we covered 5 threats to internal validity of linear regression model. The 5 threats to internal validity arose because the error term was correlated with the regressor, which caused OLS estimator of unknown population coefficients to be biased. 2 of those threats to validity are: Omitted variable bias Simultaneous causality bias Instrumental variables (IV) regression can be used to obtain a consistent estimator of the unknown coefficients in the presence of omitted variable bias or simultaneous causality bias. Maria Casanova Lecture 15A 0. Introduction How does IV work?  Intuition Consider the following model: Y = β + β 1 X + ε Think of the variation in X as having two sources: One part that is correlated with the error term One part that is not correlated with it IV uses one or more additional variables Z called instrumental variables or instruments to isolate the variation in X that is not correlated with ε . In this way the source of bias is avoided so that consistent estimates of β 1 can be obtained. Maria Casanova Lecture 15A 0. Introduction Example 1: omitted variable bias Consider the following model for the average test score in class j : Av test score j = β + β 1 Size j + ε Income would be an omitted variable in this model if: Income had an effect of average test scores AND Income was correlated with class size.Income was correlated with class size....
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This note was uploaded on 02/04/2010 for the course ECON 103 taught by Professor Sandrablack during the Spring '07 term at UCLA.
 Spring '07
 SandraBlack

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