ECO375F - 1.0 - Derivation of the OLS Estimator
ECO375F - 3.1 - Multiple Linear Regression: Partialling Out Approach
ECO375F - 3.3 -Partialling Out Approach Example
ECO375F - 2.1 - Simple Linear Regression Model Assumptions
ECO375F - 5.3 - Law of Large Numbers
ECO375F - 3.2 - Multiple Linear Regression: Unbiasedness
ECO375F - 2.5 - Important Variance and Covariance Rules
ECO375F - 6.1 - Instrumental Variables: Definition and Derivation of the Estimator
ECO375F - 5.1 - Definition of consistency
ECO375F - 4.1 - Multiple Linear Regression: Conditional Variance
ECO375F - 2.4 - Unbiasedness of the Intercept Estimator (β0)
ECO375F - 6.2 - Wald Estimator (Binary Instrumental Variable)
ECO375F - 2.3 - Unbiasedness of the Slope Estimator (β1)
ECO375F - 5.4 - Proof of consistency for the OLS estimator
ECO375F - 2.6 - Variance of the Slope Estimator (β1)
ECO375F - Exam Solution 2014 Mideterm - Question 1 (OLSE)
ECO375F - 5.2 - Useful facts about plims + continuous mapping theorem
ECO375F - 2.2 - Unbiasedness and Law of Iterated Expectations (LIE)
Deriving the OLS formula
5.6 - OLS Asymptotics: Efficiency
The properties of the OLS estimator
Assumption of exogeneity
Simple Linear Regression Derivation of OLS Estimators
Econometrics 3c: Finite sample properties
Derivation of OLS Coefficients
