2 Syllabus

2.1 General Course Description

This course introduces Economics majors to more advanced commands and techniques used in econometric software package R commonly used in empirical research.

2.2 Specific Course Description

This course familiarizes Economics majors with advanced techniques in R for empirical research. Students will also gather and analyze their own real-world data to investigate a specific economic problem, applying econometric methods and presenting their findings in a final project.

2.3 Final Course Outputs

CLOs Output Due Date
CLO 1,2,4 Data Storytelling (OR) Week 10
CLO 1-4 Data Story Archive (PF/PR) 18:00 Tuesday Week 14
CLO 1-3 Problem Sets (QA) 18:00 Weeks 6 and 12
CLO 1,3 Short Quizzes (QA) Weeks 2-6, 8, 11-12
CLO 4 Class Participation (TW/OB) Weeks 1-12

2.4 Generative AI-Use Policy

For each component of the final grade defined above, students must identify the generative AI usage policy level. The levels are:

  1. Free to Use – AI may be used without restriction.

  2. Allowed in Specific Contexts – AI may be used only for clearly defined purposes and must be cited.

  3. Banned – AI use is strictly prohibited.

Grade Component Usage Policy Level Notes
Data Storytelling (OR) Allowed in Specific Contexts Students may use generative AI only to brainstorm ideas, create outlines, and design slides. AI must not be used to generate or check final code or analysis.
Data Story Archive (PF/PR) Banned AI use is strictly prohibited for writing, coding, or analyzing the archive. Work must reflect independent technical skill.
Problem Sets (QA) Banned Students must independently write and debug code; AI may not generate or check solutions.
Short Quizzes (QA) Banned AI use is not allowed during quizzes.
Class Participation (TW/OB) Banned All participation must be based on students’ own understanding and contributions.

This course is an applied coding and econometrics laboratory, designed to develop students’ ability to analyze real-world data, write reproducible code, and interpret econometric results independently.

To support student learning and promote the responsible use of emerging technologies, students may use generative AI tools (e.g., ChatGPT, Gemini, etc.) only for conceptual or exploratory coding assistance, such as:
● Understanding function syntax or usage
● Exploring coding strategies
● Learning general debugging approaches

The use of generative AI is strictly prohibited for the following:
● Generating or checking final code for the Data Story Archive (report and code)
● Generating or checking code for Problem Sets
● Short quizzes
● Any submission intended to reflect independent technical skill

Overreliance on AI may hinder students’ understanding of key concepts, which are essential to success in this course. Any unauthorized use of AI will be treated as academic dishonesty, in accordance with the university’s academic integrity policy.

Students are encouraged to consult the instructor if they are unsure about the appropriate use of AI for specific tasks.

2.5 Classroom Policies

1. Assessments

All major assessments are described below. Deadlines, formats, and submission requirements are non-negotiable.

Assessment Format & Submission Notes
Short Quizzes In-class, 2 coding questions per session. Students work with a buddy to check each other’s answers. Each student underlines economic theory and econometric reasoning in their buddy’s paper and places check marks for each. No quizzes on Weeks 6,7, 8, 9, 10, 12, 13, 14.
Problem Set 1 Group submission (hard copy). Handwritten discussion + typed step-by-step R code + printed/pasted graphs. Covers material up to descriptive statistics. Due Week 6. Questions are provided on Day 1 in the LBOMETR Course Book.
Problem Set 2 Group submission (hard copy). Handwritten discussion + typed step-by-step R code + printed/pasted graphs. Covers material from descriptive statistics to formal tests of assumptions. Due Week 12. Questions are provided on Day 1 in the LBOMETR Course Book.
Data Storytelling (Oral Presentation) Group presentation in HyFlex classroom. Must include R visualizations and clear explanation of methodology, results, and conclusions. Graded individually, based on contribution, clarity, and engagement.
Data Story Archive Group submission (hard copy) compiling R scripts, analyses, visualizations, and interpretations. Demonstrates independent mastery; must be concise, reproducible, and complete. AI or outside generation is not allowed. Hard copy must be submitted by Week 14.

2. Groupings

  • Students will be divided into 5 groups of 6 on the first day of class.

  • Groups are assigned based on in-class skill surveys: comfort with R and economic theory.

  • Groups remain fixed throughout the semester.

  • Roles (e.g., introduction, data cleaning, analysis, visualization, discussion) may rotate within the group for fairness, except for the assigned monitor if used.

3. Appointments & Consultations

  • Data Story Topic Consultation:

    • Groups must meet with the professor before Week 5.

    • Topic, methodology, or scope changes after Week 6 without notice will incur a 50% deduction on the Data Story grade.

  • Mock Presentation & Archive Consultation:

    • Groups are encouraged to schedule 15-minute in-person sessions after Mock Presentation submission to improve the Data Story Archive.

    • Slots are booked via a provided link.

  • Other Consultations:

    • Can be done via email.

    • Lecturer responds only between 8 AM – 6 PM.

4. Grading Notes

  • Perfect scores (100%) are very rare. Excellent students typically achieve up to ~95%.

  • Grades reflect demonstrated mastery, quality of outputs, and adherence to rubrics.

  • Attendance, participation, buddy system compliance, and group contributions are part of the class participation grade.

  • No general incentives or extra credit are provided.

5. Class Monitor Responsibilities

  • One student monitor per session, rotating weekly or biweekly, will oversee the buddy system.

  • Responsibilities:

    1. Collect all buddy-checked papers after class.

    2. Record for each student:

      • Buddy’s name who checked the paper.

      • Number of checks received (0, 1, or 2).

    3. Ensure all buddy system procedures are followed (underlines and check marks).

    4. Submit a tally sheet to the professor by 6 PM of class day.

  • Important:

  • The monitor does not grade anything.

  • Failure to follow these steps may affect participation points for the monitor or the class.

6. Attendance

  • Attendance is monitored during in-person sessions.

  • Attendance points contribute to class participation.

  • Unexcused absence will not be able to have full marks in class participation and might miss short quizzes.

  • Excused absence: no deduction (documentation required).

7. Buddy System

  • Students must complete buddy checks for short quizzes as described in Section 1.

  • Compliance is verified by the class monitor and reflected in participation scores.

8. Problem Sets

  • Students are expected to complete and submit the assigned problem sets on time, including discussion, code, and visualizations as described above.

9. Data Storytelling & Data Story Archive

  • Follow all guidelines for oral presentations and written archive submissions, including hard copy submission and proper documentation of R code.

  • Consultations are highly recommended to refine presentations and archive outputs.

10. Use of Course Materials

  • All learning and teaching materials provided in this class are for the exclusive use of students enrolled in this course, Term 2, AY2025-26.

  • Students are not allowed to upload, share, or distribute these materials publicly or to anyone other than their groupmates/classmates without the instructor’s permission.

2.5.1 EXCUSED ABSENCES POLICY:

Students must process requests for excused absences from their respective Associate Deans. For SOE students, the Associate Dean of the School of Economics will only process requests for excused absences due to medical and mental health reasons. Covid-related leave requests are no longer accepted as of August 30, 2023. Students must provide official documentation from the Office of Student Affairs (OSA) for absences related to official university functions. 

Procedure:

  1. Timing: Submit requests immediately upon returning to campus and no later than seven working days from the return date. 

  2. Request Letter: Write a letter to the Associate Dean including:

○ Course details (course name, section, faculty names, and emails)

○ Dates of absence(s)

○ Reason for absence with relevant details

  1. Supporting Documents: Attach validated documents from the appropriate university offices.

  2. Submission: Combine the letter and supporting documents into a single PDF and upload viathis form.

Approved absences will be communicated to you and your professors within three working days. Note that processing is done only during regular weekday office hours. 

Important: Only complete requests will be processed. Falsifying records is a major offense as per the Student Handbook (Section 9, pp. 85-87).

Note: The syllabus is unique to the course per Term, per AY. Final syllabus uploaded in Animospace.