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