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
