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【极致中配】计量经济学研究生课程

以矩阵形式系统推导计量经济学：OLS估计、高斯-马尔可夫定理、GLS、SURE，并深入Rubin因果模型与倾向得分匹配等因果推断方法。

难度
难度 5/5 — 研究生水平，全程矩阵推导与因果推断证明，需扎实数理基础
适合人群
有数理基础、计划深入计量经济学理论的研究生或高年级本科生
前置要求
线性代数（矩阵运算、向量微分、投影、迹与秩）、概率论与数理统计（期望、方差、随机向量、无偏估计）、微积分（对向量求导）、初级计量经济学（了解OLS与回归基本概念）
课程规模
74 讲 · 1270播放
在 B 站免费观看 →就这门课提问 →排进我的学习路线

主题覆盖

计量经济学矩阵形式向量微分普通最小二乘OLS随机向量期望与方差高斯-马尔可夫定理最小二乘几何解释与正交投影误差方差估计异方差与自相关广义最小二乘GLS克罗内克积与SURERubin因果模型倾向得分匹配

课程大纲（74 讲）

  1. P1 · Introduction to the matrix formulation of econometrics4 分钟
  2. P2 · The matrix formulation of econometrics - example4 分钟
  3. P3 · How to differentiate with respect to a vector - part 14 分钟
  4. P4 · How to differentiate with respect to a vector - part 24 分钟
  5. P5 · How to differentiate with respect to a vector - part 32 分钟
  6. P6 · Ordinary Least Squares Estimators - derivation in matrix form - part 14 分钟
  7. P7 · Ordinary Least Squares Estimators - derivation in matrix form - part 24 分钟
  8. P8 · Ordinary Least Squares Estimators - derivation in matrix form - part 33 分钟
  9. P9 · Expectations and variance of a random vector - part 12 分钟
  10. P10 · Expectations and variance of a random vector - part 23 分钟
  11. P11 · Expectations and variance of a random vector - part 32 分钟
  12. P12 · Expectations and variance of a random vector - part 41 分钟
  13. P13 · Least Squares as an unbiased estimator - matrix formulation2 分钟
  14. P14 · Variance of Least Squares Estimators - Matrix Form3 分钟
  15. P15 · The Gauss-Markov Theorem proof - matrix form - part 12 分钟
  16. P16 · The Gauss-Markov Theorem proof - matrix form - part 23 分钟
  17. P17 · The Gauss-Markov Theorem proof - matrix form - part 33 分钟
  18. P18 · Geometric Interpretation of Ordinary Least Squares: An Introduction3 分钟
  19. P19 · Geometric Interpretation of Ordinary Least Squares: An Example4 分钟
  20. P20 · Geometric Least Squares Column Space Intuition3 分钟
  21. P21 · Geometric intepretation of least squares - orthogonal projection2 分钟
  22. P22 · Geometric interpretation of Least Squares: geometrical derivation of estimator2 分钟
  23. P23 · Orthogonal Projection Operator in Least Squares - part 12 分钟
  24. P24 · Orthogonal Projection Operator in Least Squares - part 22 分钟
  25. P25 · Orthogonal Projection Operator in Least Squares - part 33 分钟
  26. P26 · Estimating the error variance in matrix form - part 12 分钟
  27. P27 · Estimating the error variance in matrix form - part 22 分钟
  28. P28 · Estimating the error variance in matrix form - part 32 分钟
  29. P29 · Estimating the error variance in matrix form - part 41 分钟
  30. P30 · Estimating the error variance in matrix form - part 52 分钟
  31. P31 · Estimating the error variance in matrix form - part 62 分钟
  32. P32 · Proof that the trace of Mx is p1 分钟
  33. P33 · Representing homoscedasticity and no autocorrelation in matrix form - part 13 分钟
  34. P34 · Representing homoscedasticity and no autocorrelation in matrix form - part 23 分钟
  35. P35 · Representing heteroscedasticity in matrix form3 分钟
  36. P36 · BLUE estimators in presence of heteroscedasticity - GLS - part 12 分钟
  37. P37 · BLUE estimators in presence of heteroscedasticity - GLS - part 23 分钟
  38. P38 · GLS estimators in matrix form - part 12 分钟
  39. P39 · GLS estimators in matrix form - part 23 分钟
  40. P40 · GLS estimators in matrix form - part 33 分钟
  41. P41 · The variance of GLS estimators3 分钟
  42. P42 · GLS - example in matrix form4 分钟
  43. P43 · GLS estimators in the presence of autocorrelation and heteroscedasticity in matr3 分钟
  44. P44 · The Kronecker Product of two matrices - an introduction3 分钟
  45. P45 · SURE estimation - an introduction - part 14 分钟
  46. P46 · SURE estimation - an introduction - part 22 分钟
  47. P47 · SURE estimation - autocorrelation and heteroscedasticity5 分钟
  48. P48 · SURE estimator derivation - part 13 分钟
  49. P49 · SURE estimator derivation - part 22 分钟
  50. P50 · Kronecker Matrix Product - properties2 分钟
  51. P51 · SURE estimator - same independent variables - part 13 分钟
  52. P52 · SURE estimator - same independent variables - part 23 分钟
  53. P53 · SURE estimator - same independent variables - part 34 分钟
  54. P54 · Causality - an introduction2 分钟
  55. P55 · The Rubin Causal model - an introduction5 分钟
  56. P56 · Causation in econometrics - a simple comparison of group means4 分钟
  57. P57 · Causation in econometrics - selection bias and average causal effect4 分钟
  58. P58 · Random assignment - removes selection bias3 分钟
  59. P59 · How to check if treatment is randomly assigned?3 分钟
  60. P60 · The conditional independence assumption: introduction3 分钟
  61. P61 · The conditional independence assumption - intuition3 分钟
  62. P62 · The average causal effect - an example5 分钟
  63. P63 · The average causal effect with continuous treatment variables6 分钟
  64. P64 · Conditional Independence Assumption for Continuous Variables4 分钟
  65. P65 · Linear regression and causality7 分钟
  66. P66 · Selection bias as viewed as a problem with samples6 分钟
  67. P67 · Sample balancing via stratification and matching7 分钟
  68. P68 · Propensity score - introduction and theorem6 分钟
  69. P69 · The Law of Iterated Expectations: an introduction2 分钟
  70. P70 · The Law of Iterated Expectations: introduction to nested form3 分钟
  71. P71 · Propensity score theorem proof - part 12 分钟
  72. P72 · Propensity score theorem proof - part 23 分钟
  73. P73 · Propensity score matching: an introduction5 分钟
  74. P74 · Propensity score matching - mathematics behind estimation4 分钟

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