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