NTU Course
Overview

Experimental Economics II: Replications

Offered in 114-2

  • Serial Number

    49317

  • Course Number

    ECON5233

  • Course Identifier

    323 U8450

  • No Class

  • 2 Credits

  • Elective

    DEPARTMENT OF ECONOMICS / GRADUATE INSTITUTE OF ECONOMICS

      Elective

    • DEPARTMENT OF ECONOMICS

    • GRADUATE INSTITUTE OF ECONOMICS

  • JOSEPH TAO-YI WANG
  • Fri 7, 8

  • 社科研608

  • Type 1

  • 24 Student Quota

    NTU 22 + non-NTU 2

  • No Specialization Program

  • English
  • NTU COOL
  • Core Capabilities and Curriculum Planning
  • Notes

    The course is conducted in English。 The course is conducted in English。

  • Limits on Course Adding / Dropping

    • Restriction: juniors and beyond or Restriction: MA students and beyond or Restriction: Ph. D students

  • NTU Enrollment Status

    Enrolled
    0/22
    Other Depts
    0/4
    Remaining
    0
    Registered
    0
  • Course Description
    This is an English capstone course of experimental economics at the upper division and graduate level, focusing on replications. The purpose is to computationally replicate several assigned experimental papers, and choose a particular paper to propose a replication experiment. You may present papers in groups of two, but are required to submit replication exercises and final reports individually.
  • Course Objective
    Specific course goals include: 1. Present Contemporary Research: Students are expected to read journal articles or working papers, evaluate their quality, present them, and provide feedback to others. 2. Complete Replication Report: Students are expected to run the author-provided code/data to computationally replicate journal articles and write replication reports. 3. Design Replication Experiment: Students are expected to design a replication experiment and propose a pre-analysis plan. To conduct the experiment, you should pre-register the pre-analysis plan on Open Science Framework or AEA RCT Registry.
  • Course Requirement
    Pre-Requisites: This is a capstone course in economics, which assumes you know material taught in intermediate micro/macro/metrics. You will also need basic knowledge of Experimental Economics I: Behavioral Game Theory, available on NTU OCW/Coursera and in Camerer (2003), Behavioral Game Theory, Princeton University Press (BGT). Knowledge of graduate econometrics is highly recommended, but past experience with STATA/Matlab/R is sufficient for the data analysis of computational reproduction.
  • Expected weekly study hours before and/or after class
    6
  • Office Hour
    Fri16:20 - 17:00
    After class or by email appointment
  • Designated Reading
    1. List of papers to be replicated (varies every year). 2. Moffatt (2019), Experimetrics Lecture Notes for NTU mini-course: https://homepage.ntu.edu.tw/~josephw/ExpEcon_PE_19S_06_ExperimetricsLectureNotes.pdf
  • References
    3. Gilad Feldman’s replications website: http://mgto.org/pre-registered-replications/ 4. Gentzkow and Shapiro (2014), “Code and Data: A Practioneer’s Guide,” mimeo: https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf 5. Moffatt (2016), Experimetrics: Econometrics for Experimental Economics, Palgrave. 6. Instructions for Computational Reproducibility and Replication, Institute for Replication: https://i4replication.org/reproducibility.html
  • Grading
    30%

    Replication Exercise

    Weekly replication homework for each experimetrics lecture.

    20%

    Presentation

    Oral presentation and subsequent discussion of one paper.

    10%

    Feedback

    Give feedback to other presenters and peer review others’ plans.

    40%

    Final Report

    Final replication report or pre-analysis plan due at final week.

    1. NTU has not set an upper limit on the percentage of A+ grades.
    2. NTU uses a letter grade system for assessment. The grade percentage ranges and the single-subject grade conversion table in the NATIONAL TAIWAN UNIVERSITY Regulations Governing Academic Grading are for reference only. Instructors may adjust the percentage ranges according to the grade definitions. For more information, see the Assessment for Learning Section。
  • Adjustment methods for students
    Adjustment MethodDescription
    A2

    以錄影輔助

    Assisted by video

    B1

    延長作業繳交期限

    Extension of the deadline for submitting assignments

  • Make-up Class Information
  • Course Schedule
    2/27Week 1National Holiday Long Weekend (Watch Basic Principles of Experimental Design (BGT A1.2) on OCW)
    3/6Week 2Introduction to Replications and Computation Reproduction (Lin et al., 2020) Introduction to Experimetrics and Power Analysis (Emt 1.1–1.3)
    3/13Week 3Power Analysis with Real Examples and Monte Carlo (Emt 1.4, 2.1–2.3)
    3/20Week 4Estimating Risk Aversion: Structural Models of Binary Lottery (Emt 3.1, 3.3-3.10) Estimating Risk Aversion: Analyzing Ultimatum Game Data (Emt 3.2, 3.11)
    3/27Week 5Estimating Social Preferences (Emt 4.1–4.3)
    4/3Week 6National Holiday Long Weekend
    4/10Week 7Experimental Software and Pre-Analysis Plan (by TA at TASSEL)
    4/17Week 8Finite Mixture Models: Level-k Models, Cognitive Hierarchy and Social Preferences for Public Goods (Emt 5.1-5.5, Experimetrics 17.3)
    4/24Week 9Computational Reproduction (by TA; Andersen et al. 2011 via Jupyter Notebook)
    5/1Week 10Labor Day Holiday (Midterm Pre-analysis Plan Submission)
    5/8Week 11Estimating Repeated Game Play and QRE (Experimetrics 16.1–16.6)
    5/15Week 12Learning (BGT6); Estimating Learning (Experimetrics 18.1–18.8)
    5/22Week 13Final Replication Report Presentation; Coordination (BGT7)
    5/29Week 14Final Replication Report Presentation; Signaling and Reputation (BGT8)
    6/5Week 15Final Replication Report Due