What Does "Identification" Actually Mean in Econometrics?

Ever heard "Is this identified?" in an economics seminar? Most economists can't even agree on what identification means! In this video, we break down the true definition of identification and why it's the foundation of ALL econometric methods - from RCTs to structural models.

Scientific Papers Referenced:
Lewbel, Arthur. 2019. "The Identification Zoo: Meanings of Identification in Econometrics." Journal of Economic Literature 57 (4): 835–903.
DOI: 10.1257/jel.20181361

Key Topics Covered:
The formal definition of identification (it's about logic, not statistics!)
Why identification has nothing to do with your data - it's about the population

The 5-step hierarchy:
Identification →
Estimation →
Inference →
Hypothesis Testing →
Conclusions

Common myths about identification (debunked!)
How identification unifies the structural vs. reduced form spectrum
Why both RCTs AND structural models face the same fundamental identification challenge

Perfect for:
✓ Graduate students in economics and related fields
✓ Researchers working with causal inference
✓ Anyone trying to understand the foundations of econometrics
✓ Those confused by identification debates in seminars

What You'll Learn:
Why identification comes BEFORE estimation (and why getting it wrong makes everything else meaningless)
The difference between statistical assumptions and behavioral restrictions
Why "more data" doesn't solve identification problems
How the structural spectrum is unified by identification challenges

This is part of our series on the structural vs. reduced form debate in econometrics. Whether you're running randomized controlled trials or estimating complex equilibrium models, understanding identification is crucial for credible causal inference.

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Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!
Editing credit: @neiljohnmanllios3064