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