经济学课程

默认只展示每周独有的知识重点；节奏、考核、Tutorial 和避坑信息按需展开。

1. 1

   Course Introduction and Math Review course introduction, Matrix algebra, review of elementary probability and statistics

   第1周主题：Course Introduction and Math Review course introduction, Matrix algebra, review of elementary probability and statistics 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Course Introduction and Math Review course introduction, Matrix algebra, review of elementary probability and statistics”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   CourseIntroductionandMathReviewcourseintroduction,Matrix
2. 2

   Review of Multiple Regression and M-estimation Review matrix treatment of multiple regression; Gauss-Markov Theorem and assumptions; conditional prediction; loss function; M-estimation; causal vs. non causal relations; examples. | Tutorial 1 Review and practice the materials covered in Lecture 1.

   第2周主题：Review of Multiple Regression and M-estimation Review matrix treatment of multiple regression; Gauss-Markov Theorem and assumptions; conditional prediction; loss function; M-estimation; causal vs. non causal relations; examples. | Tutorial 1 Review and practice the materials covered in Lecture 1. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Review of Multiple Regression and M-estimation Review matrix treatment of multiple regression; Gauss-Markov Theorem and assumptions; conditional prediction; loss function; M-estimation; causal vs. non causal relations; examples. | Tutorial 1 Review and practice the materials covered in Lecture 1.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   ReviewofMultipleRegressionandM-estimationmatrix
3. 3

   Maximum Likelihood Estimation Basic likelihood concepts; score functions; computation of MLE; large sample properties; examples from univariate and regression models; likelihood-based inference. | Tutorial 2 Review and practice the materials covered in Lecture 2.

   第3周主题：Maximum Likelihood Estimation Basic likelihood concepts; score functions; computation of MLE; large sample properties; examples from univariate and regression models; likelihood-based inference. | Tutorial 2 Review and practice the materials covered in Lecture 2. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Maximum Likelihood Estimation Basic likelihood concepts; score functions; computation of MLE; large sample properties; examples from univariate and regression models; likelihood-based inference. | Tutorial 2 Review and practice the materials covered in Lecture 2.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   MaximumLikelihoodEstimationBasiclikelihoodconcepts;scorefunctions;
4. 4

   GMM Basics and Extensions Simultaneous equations framework. Essential GMM Motivation; the Analogy Principle; causal parameters; simultaneous equations; IV estimation; GMM extensions; large sample properties | Tutorial 3 Review and practice the materials covered in Lecture 3.

   第4周主题：GMM Basics and Extensions Simultaneous equations framework. Essential GMM Motivation; the Analogy Principle; causal parameters; simultaneous equations; IV estimation; GMM extensions; large sample properties | Tutorial 3 Review and practice the materials covered in Lecture 3. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“GMM Basics and Extensions Simultaneous equations framework. Essential GMM Motivation; the Analogy Principle; causal parameters; simultaneous equations; IV estimation; GMM extensions; large sample properties | Tutorial 3 Review and practice the materials covered in Lecture 3.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   GMMBasicsandExtensionsSimultaneousequationsframework.Essential
5. 5

   Linear Panel Data Models A Advantages of panel data; basics of linear panel models; pooled, random effects and fixed effect models; target parameters and estimation by GLS; applications. | Tutorial 4 Review and practice the materials covered in Lecture 4.

   第5周主题：Linear Panel Data Models A Advantages of panel data; basics of linear panel models; pooled, random effects and fixed effect models; target parameters and estimation by GLS; applications. | Tutorial 4 Review and practice the materials covered in Lecture 4. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Linear Panel Data Models A Advantages of panel data; basics of linear panel models; pooled, random effects and fixed effect models; target parameters and estimation by GLS; applications. | Tutorial 4 Review and practice the materials covered in Lecture 4.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   LinearPanelDataModelsAAdvantagesofpanel
6. 6

   Linear Panel Data Models B Extensions of basic models; types of exogeneity; endogenous regressors; dynamic models; GMM methods; application to MABEL data. Friday, April 3rd, is Good Friday, a public holiday, so there will be no classroom lectures or consultation sessions that day. A recorded lecture will be uploaded to Ultra for students to access. Students who typically attend tutorials on this day are encouraged to

   第6周主题：Linear Panel Data Models B Extensions of basic models; types of exogeneity; endogenous regressors; dynamic models; GMM methods; application to MABEL data. Friday, April 3rd, is Good Friday, a public holiday, so there will be no classroom lectures or consultation sessions that day. A recorded lecture will be uploaded to Ultra for students to access. Students who typically attend tutorials on this day are encouraged to 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Linear Panel Data Models B Extensions of basic models; types of exogeneity; endogenous regressors; dynamic models; GMM methods; application to MABEL data. Friday, April 3rd, is Good Friday, a public holiday, so there will be no classroom lectures or consultation sessions that day. A recorded lecture will be uploaded to Ultra for students to access. Students who typically attend tutorials on this day are encouraged to”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   LinearPanelDataModelsBExtensionsofbasic
7. 7

   Simulation-based Estimation and Inference Computer-intensive methods for estimation and inference; simulation-based MLE and GMM; bootstrap standard errors; applications to panel models. | Tutorial 6 Review and practice the materials covered in Lecture 6.

   第7周主题：Simulation-based Estimation and Inference Computer-intensive methods for estimation and inference; simulation-based MLE and GMM; bootstrap standard errors; applications to panel models. | Tutorial 6 Review and practice the materials covered in Lecture 6. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Simulation-based Estimation and Inference Computer-intensive methods for estimation and inference; simulation-based MLE and GMM; bootstrap standard errors; applications to panel models. | Tutorial 6 Review and practice the materials covered in Lecture 6.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   Simulation-basedEstimationandInferenceComputer-intensivemethodsforestimation
8. 8

   Quantile Regression Conditional quantiles (CQ); semiparametric models; marginal quantiles; MAD and CQ estimation; advantages of non separable heterogeneous responses; treatment effects. 18 April is a public holiday. No class, tutorial, or consultation session will be held that day. | Tutorial 7 Review and practice the materials covered in Lecture 7.

   第8周主题：Quantile Regression Conditional quantiles (CQ); semiparametric models; marginal quantiles; MAD and CQ estimation; advantages of non separable heterogeneous responses; treatment effects. 18 April is a public holiday. No class, tutorial, or consultation session will be held that day. | Tutorial 7 Review and practice the materials covered in Lecture 7. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Quantile Regression Conditional quantiles (CQ); semiparametric models; marginal quantiles; MAD and CQ estimation; advantages of non separable heterogeneous responses; treatment effects. 18 April is a public holiday. No class, tutorial, or consultation session will be held that day. | Tutorial 7 Review and practice the materials covered in Lecture 7.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   QuantileRegressionConditionalquantiles(CQ);semiparametricmodels;marginal
9. 9

   Hypothesis Tests Key concepts related to hypothesis tests, Wald, criterion-based, and score tests. | Tutorial 8 Review and practice the materials covered in Lecture 8.

   第9周主题：Hypothesis Tests Key concepts related to hypothesis tests, Wald, criterion-based, and score tests. | Tutorial 8 Review and practice the materials covered in Lecture 8. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Hypothesis Tests Key concepts related to hypothesis tests, Wald, criterion-based, and score tests. | Tutorial 8 Review and practice the materials covered in Lecture 8.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

   HypothesisTestsKeyconceptsrelatedtohypothesistests,
10. 10

    Non-parametric and Flexible Parametric Methods A Kernel density and regression; mixture models; kernel regression; mixture of normals; inference on mixture models 5 May is a public holiday. No class, tutorial, or consultation session will be held that day. | Tutorial 9 Review and practice the materials covered in Lecture 9.

    第10周主题：Non-parametric and Flexible Parametric Methods A Kernel density and regression; mixture models; kernel regression; mixture of normals; inference on mixture models 5 May is a public holiday. No class, tutorial, or consultation session will be held that day. | Tutorial 9 Review and practice the materials covered in Lecture 9. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Non-parametric and Flexible Parametric Methods A Kernel density and regression; mixture models; kernel regression; mixture of normals; inference on mixture models 5 May is a public holiday. No class, tutorial, or consultation session will be held that day. | Tutorial 9 Review and practice the materials covered in Lecture 9.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

    Non-parametricandFlexibleParametricMethodsAKerneldensity
11. 11

    Non-parametric and Flexible Parametric Methods B Kernel density and kernel regression; mixture of normals; inference on mixture models; relationship to semiparametric models; random effects and mixed models. | Tutorial 10 Review and practice the materials covered in Lecture 10.

    第11周主题：Non-parametric and Flexible Parametric Methods B Kernel density and kernel regression; mixture of normals; inference on mixture models; relationship to semiparametric models; random effects and mixed models. | Tutorial 10 Review and practice the materials covered in Lecture 10. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Non-parametric and Flexible Parametric Methods B Kernel density and kernel regression; mixture of normals; inference on mixture models; relationship to semiparametric models; random effects and mixed models. | Tutorial 10 Review and practice the materials covered in Lecture 10.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

    Non-parametricandFlexibleParametricMethodsBKerneldensity
12. 12

    Big Data and Machine Learning High dimensional regression; Ridge regression; LASSO; penalty variable selection; LASSO IV; double/debiased machine learning. | Tutorial 11 Review and practice the materials covered in Lecture 11.

    第12周主题：Big Data and Machine Learning High dimensional regression; Ridge regression; LASSO; penalty variable selection; LASSO IV; double/debiased machine learning. | Tutorial 11 Review and practice the materials covered in Lecture 11. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Big Data and Machine Learning High dimensional regression; Ridge regression; LASSO; penalty variable selection; LASSO IV; double/debiased machine learning. | Tutorial 11 Review and practice the materials covered in Lecture 11.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

    BigDataandMachineLearningHighdimensionalregression;
13. 13

    Review Lecture Review lecture | Tutorial 12 Review and practice the materials covered in Lecture 12.

    第13周主题：Review Lecture Review lecture | Tutorial 12 Review and practice the materials covered in Lecture 12. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Review Lecture Review lecture | Tutorial 12 Review and practice the materials covered in Lecture 12.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 ECON7320 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

    ReviewLecturelectureTutorial12and