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Applied Econometrics
FRE 528
Instructor: Michael Johnson
Juan Fercovic (Course support); Email:
Office: Room: McMl 231
Office Hours: Tuesdays following class for 1 hour (Room: McMl 231)
Class Schedule: Lectures: Tues and Thurs 3:30 to 5:00pm (McMl 154)

Labs: Thurs 5:00 – 6:00pm Room 192 MacMillan

Classroom: Lectures: McMl 154

Labs: Room 192 MacMillan

Important Course Pages
Lecture Notes
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 carry out a team assignment and presentation to further contribute to the understanding and application of applied econometrics.

Course Website:

Learning Outcomes

  • Develop a broad understanding of regression analysis using cross-sectional data relevant for analysing economic and business data.  Fully understand the underlying assumptions of OLS and mitigation strategies when assumptions are violated.
  • Understand the context of applied econometrics to prediction and theory driven models.
  • Specify, interpret and critically evaluate regression estimates using procedures of diagnostic testing and model validation.
  • Understand important theoretical properties of ordinary least squares estimators and the statistical testing of hypotheses with regards to econometric modeling.
  • Perform statistical tests to investigate whether the classical assumptions in regression analysis are satisfied, and what to do when such assumptions are violated.
  • Understand the context of estimation using method of moments and the maximum likelihood principle for parameter estimation.
  • Become proficient in the use and application of Stata for conducting econometric analysis and Excel for data manipulation and conducting classical statistical tests.  Become proficient in the use and application of Tableau as a visualization tool.
  • Evaluate academic literature concerning empirical analysis and econometrics.  Develop critical thinking skills as a reader of journal articles that make use of the concepts and methods that are introduced in the course.
  • Become proficient in the development of econometric models to your own academic work and internship project.


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).

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 squares method). Programmable calculators are not allowed during examinations.


Assignments 20%
Team Assignment and Presentation 10%
Midterm Exam (Tuesday October 24th) 25%
Final Exam 40%
Participation and TBL (Individual and Team-based Exercises) 5%

Makeup Exams or Tests

There will be NO makeup exams or quizzes.  If you miss an exam, you will receive zero marks. Likewise, late assignments will be heavily penalized and will be discounted by 50% per day. 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 (not after the exam or deadline has taken place).  Notification after the examination date is not acceptable.

Team Assignment and Presentation (10%)

Student will be placed in one of ten 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. As econometrics is a vast area of research, papers selected for this presentation should focus on ordinary least squares (simple regression or multiple regression) or models using qualitative dependent variables (logistic/probit/etc models).  All papers need to be approved by the instructor at least one week prior to the presentation date.  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 yet 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 or another area of interest for the team (sports, finance etc).
  • 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 (Have the authors provided information to support them? What evidence do they provide? Is information missing? How would you improve the models developed?). Discuss the application and limitations of the model(s) presented.
  • Discuss the results of the study and its implications.
  • 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 Canvas.

The evaluation of this project will consist of both a team mark, an individual grade and a peer evaluation.  The team grade will be based on the information presented (content, clarity, ability to add value to class discussion and its content). The individual grade is based on your individual ability to communicate the details of your paper and answer questions while presenting.  After the presentation, a peer evaluation will be provided to all team members to evaluate your contribution to the research project and the presentation.  Team presentations will take place during the following weeks:

  • Week 9: Three teams to present
  • Week 13: Six teams to present

Statistical Software

Excel, Tableau and Stata will be used in this course. Stata will be extensively used for econometric modeling.  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 $48 USD online at:

Note: If you considering taking FRE 529, the 6-month license will be sufficient, but you may want to consider a yearly license if you see doing econometrics for your internship next summer! Please download your free student copy of Tableau Desktop using the following link:

Mike will be sending you an activation code via email to allow you to activate your license.  

Attendance: Required

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 this week’s class on Thursday.  Please bring a hard copy only - not emailed.

Academic Honesty

Academic honesty is essential to the continued functioning of the University of British Columbia as an institution of higher learning and research. All UBC students are expected to behave as honest and responsible members of an academic community. Breach of those expectations or failure to follow the appropriate policies, principles, rules, and guidelines of the University with respect to academic honesty may result in disciplinary action.

Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:

  • Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
  • Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
  • Submitting others work as your own, may include but not limited to i. using, or attempting to use, another student’s answers; ii. providing answers to other students; iii.  failing to take reasonable measures to protect answers from use by other students; or iv. in the case of students who study together, submitting identical or virtually identical assignments for evaluation unless permitted by the course instructor.
  • Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
  • Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.

Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non-acceptable conduct for examinations and graded assignments as presented via FRE code of conduct guidelines; course syllabus and instructors; and UBC academic misconduct policies, Review the following web sites for details:

Penalties for Academic Dishonesty: Academic misconduct is treated as a serious offence at UBC and within the MFRE program. Penalties for academic dishonesty are applied at the discretion of the course instructor. Incidences of academic misconduct may result in a reduction of grade or a mark of zero on the assignment or examination with more serious consequences being applied if the matter is referred to the Dean's office and/or President's Advisory Committee on Student Discipline. Careful records are kept to ensure monitoring and prevent recurrences.

Tentative Schedule

Week Lecture Topics Readings
1 - Sept 2 Introduction to course and econometrics. Introduction to data visualization using Tableau.
2 - Sept 9 More Tableau. Class survey. Review of inferential statistics;

estimation; hypothesis testing and Excel (Data Analysis


3 - Sept 16 Pivot Table Analysis. Excel/Stata. Introduction to regression. Handouts

Hill Chapt 1-4

4 - Sept 23 Regression Basics: the simple linear regression model
5 - Sept 30 Regression Basics: interval estimation and hypothesis testing;

prediction; goodness of fit; functional forms; interpretation and

modeling issues

6 – Oct 7 Regression Basics: case study.
7 – Oct 14 Multivariate Regression (Introduction to multivariate analysis) Hill Chapt 5
8 - Oct 21 Thurs Oct 24th – MIDTERM EXAM
9 – Oct 28 Team Presentations. Multivariate Regression Hill Chapt 6-8
10 - Nov 4 Multivariate Regression
11 - Nov 11 Multivariate Regression; start of Qualitative Dependent

Variables (Logit, Probit, etc)

12 - Nov 18 Qualitative Dependent Variables Hill

Chapt 16

13 - Nov 25 Team Presentations

(Dec 3rd – note: class this day will go from 3:30pm to 6:00pm

in order to complete all teams presentation

– please plan accordingly)