Course:FRE528

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Applied Econometrics
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FRE 528
Section:
Instructor: Michael Johnson
Juan Fercovic
Email: mjohnson@mail.ubc.ca
Office: MCML 231 or MCML 192 computer lab
Office Hours: Fridays following class
Class Schedule: Lecture: Tue 10:30 - 12:00, Fri 10:30 - 12:00

Lab: Fri 12:00 - 1:00

Classroom: Lecture: (Tue, FNH 50), (Fri, MCML 154)

Lab: McML 192

Important Course Pages
Syllabus
Lecture Notes
Assignments
Course Discussion


Course Overview

This course will provide the necessary foundations and experience for students to conduct sound empirical research in Food and Resource Economics. The course will review the foundations of data and regression analysis and the common problems encountered by applied researchers (data constraints and econometric challenges) along with potential solutions to these problems. Students will be expected to manipulate data and apply the models presented in class on a weekly basis with assignments and lab sessions. Additionally, students will conduct a research project in an area of their interest.

Textbook/References

R. Carter Hill, William E. Griffiths and Guay C. Lim, Principles of Econometrics, Fourth Edition, Wiley, 2011. (This textbook is on course reserve in Koerner Library, reserve collection, 3rd floor, Call Number: HB139 .H548 2011).

https://go.library.ubc.ca/HxzSGV

https://cr.library.ubc.ca/get/course/66966/hash/i.VkF8ZC

Albright, S., Winston, W. and Zappe, C. Data Analysis and Decision Making, 4th Edition, South Western, 2011.

A Stata guide for the textbook is also on reserve at the same location:

Using Stata for Principles of Econometrics, 4th edition by Lee C. Adkins and R. Carter Hill.

Required Calculator

Any 2-variable Statistics (Sharp EL 531 or equivalent). Your calculator must be able to perform simple linear regression (least square method). Programmable calculators are not allowed during examinations.

Evaluation

Assignments 15%
Team Assignment and Presentation 15%
Midterm Exam (Tue Oct 25th) 30%
Final Project 30%
Participation (Individual and Team-based Exercises): 10%

Makeup Exams or Tests

There will be NO makeup exams or quizzes. If you miss an exam, you will receive zero marks. Exceptions may be made for documented medical reasons or extenuating circumstances. In such a case, it is the responsibility of the student to inform the instructor immediately by phone only (not after the exam has taken place). Notification after the examination date is not acceptable.

Team Assignment and Presentation

Student will be placed in one of six teams and will be asked to critically review an applied research paper that utilizes econometric modeling. This is a form of mini-research project that is put together and presented to the class. The aim is to understand the integration of theory and the application of econometric models, by examining a research paper(s) that uses a particular econometric modeling technique and sharing it with the entire class. The presentation should summarize, critique and distill the essence of the research paper(s) and the econometric model(s) presented. The format of the presentations should typically be as follows:

  • Duration 20 to 30 minutes, everyone in the group participates.
  • Presentation style is relaxed and professional.
  • Introduce the research topic, theory or question discussed. The topic should be something of interest to the class in the area of food and resource economics.
  • Discuss the econometric model(s) that was developed or used. Discuss the data source that was used. Explain some of the more important underlying properties of the model and critically analyze any underlying theoretical assumptions of the model (do they how true given the information provided or have the authors provided no information to support them?). Discuss the application and limitations of the model(s) presented.
  • Summarize the key learning points of the econometric model and its application for the class.
  • Everyone who is not presenting is expected to have read the journal paper and to come prepared to contribute and ask questions.

The class will ask questions and discuss your presentation. The research paper must be provided to Mike via email 1 week prior to the presentation to allow class members the opportunity to read the paper in advance. One day prior to the presentation, the team’s powerpoint slide should be emailed to Mike so he can post the slides in Connect. Team presentations will take place during the following weeks and will focus on the following course topics (this is tentative schedule only):

  • Week 4: Team #1 - Simple Linear regression (Chapt 2-4 of Hill)
  • Week 6: Team #2 - Multiple regression (Chapt 5-6, also potentially 7-8, of Hill)
  • Week 8: Team #3 - Multiple regression (Chapt 5-6, also potentially 7-8, of Hill)
  • Week 10: Team #4 – Time Series Forecasting Models
  • Week 12: Team #5 – Regression with Time Series (Chapter 9 and 12 of Hill)
  • Week 12: Team #6 – Logistic Regression Models

Please note that easier topics are expected to be of higher quality!


Research Project (30%)

Students will undertake a course project in teams of two students, in which they will use the techniques covered in this class to answer a research question of their own choosing. Students will identify a research question, survey the relevant literature, obtain appropriate data, analyze the data, and present conclusions to the class during the final week of classes. More information on the research project will be posted in Connect.

Statistical Software

Both Excel and Stata will be used in this course. Stata will also be demonstrated in this class as an additional econometric software to use. Lecture examples, problem sets and assignments will be presented using either Excel or Stata (or both), depending on the application. Stata can be found in the Buchanan B101 Drop-in Lab, and Buchanan B125/B126 but it is recommended to purchase your own license for $38 online at: http://www.stata.com/order/new/edu/gradplans/student-pricing/

Attendance: Required

Attendance is mandatory at ALL class. The course will be conducted using a Team-Based Learning (TBL) format, to develop both your leadership and team-building skills, while enhancing your learning beyond individual study. Your team will require access to a laptop computer during classes during TBL exercises.

“Getting to know you” Written Exercise: Please complete this first exercise and hand it directly to Mike during next week’s class on Tuesday. Please bring a hard copy only - not emailed.

Academic Integrity

The academic enterprise is founded on honesty, civility, and integrity. As members of this enterprise, all students are expected to know, understand, and follow the codes of conduct regarding academic integrity. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not cheat, copy, or mislead others about what is your work. Violations of academic integrity (i.e., misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment or exam and more serious consequences may apply if the matter is referred to the President’s Advisory Committee on Student Discipline. Careful records are kept in order to monitor and prevent recurrences. A more detailed description of academic integrity, including the University’s policies and procedures, may be found in the Academic Calendar at http://calendar.ubc.ca/vancouver/index.cfm?tree=3,54,111,0.

Tentative Schedule

Week Lecture Topics Team Presentation Readings
1 - Sept 1 Introduction
2 - Sept 8 Review of Probability and Inferential Statistics
3 - Sept 15 Regression Basics Hill: Chapter 1-4
4 - Sept 22 Regression Basics Team #1 - SLR Albright: Chapter 10-11
5 - Sept 29 Multivariate Regression Hill: Chapter 5-8
6 - Oct 6 Multivariate Regression Team #2 - Multiple Albright: Chapter 10-11
7 - Oct 13 Multivariate Regression
8 - Oct 20 MIDTERM EXAM
9 - Oct 27 Regression with Time Series Hill: Chapter 9, 12

Albright: Chapter 12

10 - Nov 3 Panel Data Models Team #3 - Time Series Hill: Chapter 15
11 - Nov 10 Limited Dependent Variables Team #4 - Panel Data Hill: Chapter 16
12 - Nov 17 Catch Up, Project Preparation Team #5 - Logistic
13 - Nov 24 Final Project Presentations