教學大綱 Syllabus

科目名稱:應用個體計量與R語言

Course Name: Applied Microeconometrics with R

修別:選

Type of Credit: Elective

3.0

學分數

Credit(s)

20

預收人數

Number of Students

課程資料Course Details

課程簡介Course Description

  • The purpose of this course is to provide an introduction to modern econometric methods commonly used in applied economics, with an emphasis on microeconometric issues based on cross-sectional and panel data.
  • We will discuss linear regression, instrumental variables, simulation, robust inference, discrete choice analysis, panel data models, causal inference, and machine learning methods for econometrics. If time permits, we will discuss distributional methods.
  • Rigor and understanding of empirical methods and techniques are emphasized as opposed to learning cookbook methods. We will use a computer program R to gain hands-on experience with working with real data and statistical/econometric programming.
  • The prerequisite for the course is introductory econometrics and statistics.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明


    課程目標與學習成效Course Objectives & Learning Outcomes

    • Students are assumed to have a good understanding of undergraduate-level econometric topics such as linear regression; heteroskedasticity; unbiasedness, consistency, efficiency, and sampling distributions of estimators/test statistics; classical inference methods; and endogeneity and instrumental variables.
    • We will be using R. As such, having elementary programming skills is expected.
    • At the end of this course, students are expected to:
    • have knowledge of various applied econometric models and econometric issues relevant for analyzing economic data;
    • know the theoretical background and assumptions for econometric estimation and inference methods;
    • be able to use R to perform an empirical analysis.

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type
    • The tentative course outline is as follows. The weekly coverage might change as it depends on the progress of the class.
    • Expected hours of study are 7.5 hours per week (with lecture times included).

    ---

    Week 1 (9/15) - Course Introduction & Regression Fundamentals

    Week 2 (9/22) - R Tutorials

    Week 3 (9/29) - Review of Linear Regression, Least Squares and Instrumental Variables

    Week 4 (10/6) - Review of Linear Regression, Least Squares and Instrumental Variables

    Week 5 (10/13) - Robust and Clustered Standard Errors (Empirical research proposal due)

    Week 6 (10/20) - Robust and Clustered Standard Errors

    Week 7 (10/27) - Simulation and Bootstrap Methods

    Week 8 (11/3) - Midterm exam

    Week 9 (11/10) - Discrete Choice Analysis

    Week 10 (11/17) - Discrete Choice Analysis; Basic Panel Data Models

    Week 11 (11/24) - Basic Panel Data Models

    Week 12 (12/1) - Difference in Differences

    Week 13 (12/8) - Synthetic Controls

    Week 14 (12/15) - Machine Learning for Econometrics

    Week 15 (12/22) - Machine Learning for Econometrics

    Week 16 (12/29) - Distributional methods (if time permits)

    Week 17 (1/5) - Empirical Research Project (No Class)

    Week 18 (1/12) - Empirical Research Project & Empirical research paper due (No Class)

    ---

    • Problem sets
    • The problem sets will include both problem solving and computer tasks. The assignments will be reviewed in class if necessary.
    • You are encouraged to form a study group with your classmates, but you must write up your own answers. Problem sets with identical answers will NOT be accepted.
    • Group empirical research project (with 4-5 students per group)
    • There will be a group project on empirical research, aiming to provide students with experience in applying the statistical and econometric methods examined in the course.
    • The task is to take a published article of interest, replicate its numerical results, and then extend the analysis in some way. Possible extensions include different data and modifications of model specification.
    • The empirical research paper will be evaluated with respect to clarity of exposition, thoroughness of description of the data and methods, competence in using the methods, and thoughtfulness in interpreting results. Complexity of economic theory and econometric methods does not carry weight in the evaluation.

    授課方式Teaching Approach

    90%

    講述 Lecture

    10%

    討論 Discussion

    0%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他: Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    • Class participation (10%)
    • Problem sets (30%)
    • The midterm exam (30%)
    • The empirical research proposal & paper (30%)

    指定/參考書目Textbook & References

    There is no required texts for this course. The supplementary texts listed below are optional and recommended:

    • Econometrics, by Bruce Hansen, Princeton University Press, 2022.
    • Causal Inference - The Mixtape, by Scott Cunningham, Yale University Press, 2021.
    • Mostly Harmless Econometrics: An Empiricist's Companion, by Angrist & Pischke, Princeton University Press, 2009.
    • Applied Regression Analysis & Generalized Linear Models, by John Fox, 3rd edition, SAGE Publications, Inc., 2016.
    • An R Companion to Applied Regression, by Fox and Weisberg, 3rd edition, SAGE Publications, Inc., 2019.
    • Applied Econometrics with R, by Kleiber and Zeileis, Springer-Verlag, 2008. (Chapters 1-5 & 7)
    • Introduction to Econometrics with R, by Hanck, Arnold, Gerber, and Schmelzer, 2018. (Chapters 1-7)

     

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    課程相關連結Course Related Links

    NA

    課程附件Course Attachments

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