Graduate Econometrics

Graduate Econometrics

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56 - Causation in econometrics - a simple comparison of group means

56 of 77

56 of 77

56 - Causation in econometrics - a simple comparison of group means

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Classroom Contents

Graduate Econometrics

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

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