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34 - Representing homoscedasticity and no autocorrelation in matrix form - part 2
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Classroom Contents
Graduate Econometrics
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- 1 1 - Introduction to the matrix formulation of econometrics
- 2 2 - Matrix formulation of econometrics - example
- 3 3 - How to differentiate with respect to a vector - part 1
- 4 4 - How to differentiate with respect to a vector - part 2
- 5 5 - How to differentiate with respect to a vector - part 3
- 6 6 - Ordinary Least Squares Estimators - derivation in matrix form - part 1
- 7 7 - Ordinary Least Squares Estimators - derivation in matrix form - part 2
- 8 8 - Ordinary Least Squares Estimators - derivation in matrix form - part 3
- 9 9 - Expectations and variance of a random vector - part 1
- 10 10 - Expectations and variance of a random vector - part 2
- 11 11 - Expectations and variance of a random vector - part 3
- 12 12 - Expectations and variance of a random vector - part 4
- 13 13 - Least Squares as an unbiased estimator - matrix formulation
- 14 14 - Variance of Least Squares Estimators - Matrix Form
- 15 15 - The Gauss-Markov Theorem proof - matrix form - part 1
- 16 16 - The Gauss-Markov Theorem proof - matrix form - part 2
- 17 17 - The Gauss-Markov Theorem proof - matrix form - part 3
- 18 18 - Geometric Interpretation of Ordinary Least Squares: An Introduction
- 19 19 - Geometric Interpretation of Ordinary Least Squares: An Example
- 20 20 - Geometric Least Squares Column Space Intuition
- 21 21 - Geometric intepretation of least squares - orthogonal projection
- 22 22 - Geometric interpretation of Least Squares: geometrical derivation of estimator
- 23 23 - Orthogonal Projection Operator in Least Squares - part 1
- 24 24 - Orthogonal Projection Operator in Least Squares - part 2
- 25 25 - Orthogonal Projection Operator in Least Squares - part 3
- 26 26 - Estimating the error variance in matrix form - part 1
- 27 27 - Estimating the error variance in matrix form - part 2
- 28 28 - Estimating the error variance in matrix form - part 3
- 29 29 - Estimating the error variance in matrix form - part 4
- 30 30 - Estimating the error variance in matrix form - part 5
- 31 31 - Estimating the error variance in matrix form - part 6
- 32 32 - Proof that the trace of Mx is p
- 33 33 - Representing homoscedasticity and no autocorrelation in matrix form - part 1
- 34 34 - Representing homoscedasticity and no autocorrelation in matrix form - part 2
- 35 35 - Representing heteroscedasticity in matrix form
- 36 36 - BLUE estimators in presence of heteroscedasticity - GLS - part 1
- 37 37 - BLUE estimators in presence of heteroscedasticity - GLS - part 2
- 38 38 - GLS estimators in matrix form - part 1
- 39 39 - GLS estimators in matrix form - part 2
- 40 40 - GLS estimators in matrix form - part 3
- 41 41 - The variance of GLS estimators
- 42 42 - GLS - example in matrix form
- 43 43 - GLS estimators in the presence of autocorrelation and heteroscedasticity in matrix form
- 44 44 - The Kronecker Product of two matrices - an introduction
- 45 45 - SURE estimation - an introduction - part 1
- 46 46 - SURE estimation - an introduction - part 2
- 47 47 - SURE estimation - autocorrelation and heteroscedasticity
- 48 48 - SURE estimator derivation - part 1
- 49 49 - SURE estimator derivation - part 2
- 50 50 - Kronecker Matrix Product - properties
- 51 51 - SURE estimator - same independent variables - part 1
- 52 52 - SURE estimator - same independent variables - part 2
- 53 53 - SURE estimator - same independent variables - part 3
- 54 54 - Causality - an introduction
- 55 55 - The Rubin Causal model - an introduction
- 56 56 - Causation in econometrics - a simple comparison of group means
- 57 57 - Causation in econometrics - selection bias and average causal effect
- 58 58 - Random assignment - removes selection bias
- 59 59 - How to check if treatment is randomly assigned?
- 60 60 - The conditional independence assumption: introduction
- 61 61 - The conditional independence assumption - intuition
- 62 62 - The average causal effect - an example
- 63 63 - The average causal effect with continuous treatment variables
- 64 64 the conditional independence assumption example
- 65 65 - Linear regression and causality
- 66 66 - Selection bias as viewed as a problem with samples
- 67 67 - Sample balancing via stratification and matching
- 68 68 - Propensity score - introduction and theorem
- 69 69 - The Law of Iterated Expectations: an introduction
- 70 70 - The Law of Iterated Expectations: introduction to nested form
- 71 71 - Propensity score theorem proof - part 1
- 72 72 - Propensity score theorem proof - part 2
- 73 73 - Propensity score matching: an introduction
- 74 74 - Propensity score matching - mathematics behind estimation
- 75 Method of moments and generalised method of moments - basic introduction
- 76 Method of Moments and Generalised Method of Moments Estimation - part 1
- 77 Method of Moments and Generalised Method of Moments Estimation part 2
