Introduction to Instrumental Variables (IV)

MIT's Josh Angrist introduces one of econometrics most powerful tools: instrumental variables. Instrumental variables (IV, for those in the know), allow masters of econometrics to draw convincing causal conclusions when a treatment of interest is incompletely or imperfectly randomized. For example, arguments over American school quality often run hot, boiling over with selection bias. See a school with strong graduation rates and enticing test scores? Is that a good school or just an ordinary school fortuitously located in a good neighborhood? Lotteries that randomize offers of a school seat at in-demand schools should unravel the school quality conundrum. But lotteries only offer seats. Families are free to accept or go elsewhere and these choices are far from random. IV provides a path to causal conclusions even in the face of this sort of incomplete randomization. In this video, we cover the following: - Incomplete random assignment - IV terminology: first stage, second stage, instrument, reduced form - Three key IV assumptions: substantial first stage, independence assumption, exclusion restriction ***INSTRUCTOR RESOURCES*** High school teacher resources: https://mru.io/7gg Professor resources: https://mru.io/7rq Econometrics test bank: https://mru.io/x9c EconInbox: https://mru.io/qym ***MORE LEARNING*** Try out our practice questions: https://mru.io/pl8 See the full course: https://mru.io/z3a Receive updates when we release new videos: https://mru.io/rgz More from Marginal Revolution University: https://mru.io/4my

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