14.3) Python: Diff-in-Diff (DD)

6.1) Book Review: Mostly Harmless Econometrics
https://youtu.be/iVCnm7okbD4

6.2) Mostly Harmless Econometrics: The Experimental Ideal
https://youtu.be/SwGskvezcvY

6.3) Book Review: Econometric Analysis of Cross Section and Panel Data
https://youtu.be/M1C0kvtpkKw

6.4) Why Economists created Econometrics methods rather than run Experiments?
https://youtu.be/AQXPujVLNPU

6.5) Is Regression a Necessary Tool to Analyze Experimental Data?
https://youtu.be/5XpvsZmlpNw

6.6) Book Review: A Guide to Econometrics
https://youtu.be/-dR8SgXnxQY

6.7) Book Review: Econometrics
https://youtu.be/i6RWWltMc-Q

6.8) Introductory Books for Econometrics
https://youtu.be/MbUHJKkEuUE

6.9) Mathematical Exposition of Why Random Assignment Eliminates Selection Bias
https://youtu.be/K6vUkvQO5Ik

6.10) Regression Analysis of Experiments
https://youtu.be/3LX1PRxe9_c

6.11) Field Centipedes
https://youtu.be/iif2h4tOzVE

6.12) Bias Caused by Bad Controls
https://youtu.be/oERr0EbmfAE

6.13) Structural Econometrics vs Experiment
https://youtu.be/d4WN3O9cx8M

6.14) Are Emily and Greg More Employable Than Lakisha and Jamal?
https://youtu.be/dYWNst4ZWuQ

6.15) Times Series vs Cross Section vs Panel Data
https://youtu.be/U6LNfQIYCsc

7.1) Criteria for Estimators: Unbiasedness
https://youtu.be/qmP1NzgC4iM

7.2) Criteria for Estimators: Efficiency
https://youtu.be/XQiku381PHk

7.3) Criteria for Estimators: Mean Square Error (MSE)
https://youtu.be/x0qdhdjKyzc

7.4) Asymptotic Properties of Estimators
https://youtu.be/-79gzQkgXpQ

7.5) Intuition: Maximum Likelihood Estimator
https://youtu.be/Q8GyTmXp33M

7.6) Simple vs Multiple Regression
https://youtu.be/T7jvfJ1FHf0

7.7) T-Test vs F-Test: Joint Hypothesis
https://youtu.be/XyerB_btyDk

8.1) Law of Iterated Expectation
https://youtu.be/v_wK5ezErjc

8.2) Geometric Interpretation of OLS
https://youtu.be/4rxSesLVgBA

8.3) Ordinary Least Squares: Key Assumption
https://youtu.be/I6f1MIg7Ncc

8.4) Conditional Independence Assumption (CIA)
https://youtu.be/GsKc7jRuJgE

8.5) Unconditional vs Conditional Variance
https://youtu.be/a23uKAmDIdA

8.6) Homoskedastic vs Heteroskedasticity Errors
https://youtu.be/cFhASmYVb-U

9.1) Minimize the Residual Sum of Squares (RSS)
https://youtu.be/P6oIYmK4XdI

9.2) OLS Matrix Notation
https://youtu.be/7cvHBEQ9Tn8

9.3) Projection Matrix: Idempotent and Symmetric
https://youtu.be/N74xITRK2lU

9.4) Orthogonal Projection Matrix
https://youtu.be/OF0vlvbNbL0

9.5) Derivation of R-Squared
https://youtu.be/KPhpC-QnTLY

9.6) Orthogonal Partitioned Regression
https://youtu.be/1h9i7vDBvCg

10.1) Unbiasedness of OLS
https://youtu.be/G5RLYF19gao

10.2) Consistency of OLS
https://youtu.be/9FPSrWOvRFg

10.3) OLS: Variance
https://youtu.be/IvL4aQC14zQ

10.4) Weighted Least Squares (WLS)
https://youtu.be/_n3Z_E3g3Rg

10.5) Generalized Least Squares (GLS)
https://youtu.be/cuARqzNKLq0

11.1) Omitted Variable Bias: Proxy Solution
https://youtu.be/ERbkFE4YaMo

11.2) Measurement Error in the Dependent Variable
https://youtu.be/5fBvt8Kkt4Q

11.3) Measurement Error in an Explanatory Variable
https://youtu.be/lm20cLHn4lE

11.4) Classical Errors-in-Variables and Attenuation Bias
https://youtu.be/43W-ub9rDdc

12.1) Instrumental Variables (IV): Assumptions
https://youtu.be/GT60wWNbLeA

12.2) Why Instrumental Variable?
https://youtu.be/5U2YxKufepg

12.3) Two-Stage Least Squares (2SLS)
https://youtu.be/qTX1lg8QKQ8

12.4) Python: IV and 2SLS
https://youtu.be/DPFxUjCJE7k

13.1) Sharp Regression Discontinuity
https://youtu.be/_PQYKDrBU0w

13.2) Regression Discontinuity in Python
https://youtu.be/FcFYYA8iMCs

13.3) Regression Discontinuity (RD)
https://youtu.be/bgYSOtW6Www

13.4) Fuzzy Regression Discontinuity (FRD)
https://youtu.be/eJKF2kAkh1Y

13.5) Fuzzy vs Sharp RD
https://youtu.be/ec6J0AXikeQ

13.6) Python Fuzzy RD
https://youtu.be/OpjhM5EhPhk

14.1) First-Difference Estimator
https://youtu.be/p9NhSrTugYM

14.2) Algebra of Difference-in-Differences (DID)
https://youtu.be/_mOJK8A6nR0

14.3) Python: Diff-in-Diff (DD)
https://youtu.be/lxgGqV5zfnw

14.4) Quasi-Experiment Diff-in-Diff (DID)
https://youtu.be/jDEe8qhpzRc

15.1) Fixed Effects (FE): Time-Demeaned
https://youtu.be/HYTdxssnZuI

15.2) Random Effects (RE) vs Fixed Effects (FE)
https://youtu.be/L_RTB49Mmy8

15.3) Random Effects (RE) is Generalized Least Squares (GLS)
https://youtu.be/0DXfmR5K1OA

15.4) Covariance Matrix: Random Effects (RE)
https://youtu.be/XebzmI5XSuA

15.5) Random Effects as a Weighted Average of OLS and FE
https://youtu.be/eM-fRB4cE-k

15.6) Python: Fixed and Random Effects
https://youtu.be/Q0kossogTko