| Date | # | Title / Contents | Handout |
| 2024/4/12 | 1 | TA Session 1 | |
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1. Course Aims and Objectives 2. Matrix Knowledge 3. Differentiation of Matrix |
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| 2024/4/19 | 2 | TA Session 2 | |
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1. Review of Mapping 2. Review of Optimisation 3. Large Order and Small Order 4. Basic Convergence Theory |
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| 2024/4/26 | 3 | TA Session 3 | |
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1. Multivariate Normal Distribution 2. Ordinary Least Squares 3. RExercise |
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| 2024/5/10 | 4 | TA Session 4 | |
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1. Lebesgue-Stieltjes Expression 2. Markov’s inequality and Chebyshev’s Inequality 3. Law of Large Numbers 4. Characteristic Function and Moment Generating Function of a Random Variable 5. Central Limit Theorem Appendix A. Appendix B. |
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| 2024/5/17 | 5 | TA Session 5 | |
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1. Asymptotic Properties of OLSE 2. Test Statistics Appendix |
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| 2024/5/24 | 6 | TA Session 6 | PDF Exercise |
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1. Review of Some Concepts for a Multivariate Normal Random Variable 2. Multiple Regression Model 3. Gauss–Markov Theorem for a Multiple Regression Model 4. Asymptotic Normality for the OLS Estimator of a Multiple Regression Model Appendix A Appendix B |
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| 2024/5/31 | 7 | TA Session 7 | |
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1. Review of F Statistic Test 2. Constrained OLS 3. R Exercise Appendix |
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| 2024/6/7 | 8 | TA Session 8 | |
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1. Matrix Transformation 2. Generalized Least Squares Estimator 3. Gauss–Markov Theorem for the Generalized Least Squares Estimator 4. Comparison of the OLS and GLS estimator 5. Asymptotic Normality for the GLS Estimator Appendix |
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| 2024/6/14 | 9 | TA Session 9 | |
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1. GLS(cont’d) 2. M-estimation 3. Introductory Topics of the ML Method 4. R Excecise |
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| 2024/6/21 | 10 | TA Session 10 | |
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1. M–Estimation 2. Consistency and Asymptotic Normality for the MLE 3. Non–linear Optimization Procedure Appendix A Appendix B |
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| 2024/6/28 | 11 | TA Session 11 | |
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1. The MLE of a Single Regression Model 2. The MLE of a Multiple Regression Model 3. The Properties of AR(1) Model and its Estimation 4. Linear Regression Model with the Auto Correlation of the Error Term |
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| 2024/7/5 | 12 | TA Session 12 | |
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1. Review of the Asymptotic Theory 2. Review of the Asymptotic Normality of the M-estimator 3. M-estimator of the Linear Regression Model 4. R Excercise |
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| 2024/7/12 | 13 | TA Session 13 | |
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1. Endogeneity 2. Measurement Error: Example 3. Instrumental Variable 4. Identification Problem 5. Instrumental Variable Estimator 6. Partial Identification |
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| 2024/7/19 | 14 | TA Session 14 | PDF Exercise |
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1. Deriving the 2SLS Estimator 2. Properties of the 2SLS Estimator 3. R Exercise |
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| 2024/7/26 | 15 | TA Session 15 | PDF |
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1. Large Sample Tests 2. The Wald Test 3. The Score Test (Lagrange Multiplier Test) 4. The Likelihood Ratio Test 5. Summary of the Three Tests |