From UBC Wiki
Jump to navigation Jump to search
Applied Econometrics
FRE 528
Instructor: M. Johnson
Office Hours: Thurs 4-5 pm
Class Schedule: Tues and Thurs 12:00 to 1:30pm
Classroom: MCML 154
Important Course Pages
Lecture Notes
Course Discussion

Course Information

Instructor: M. Johnson, PhD

Lectures: Tues and Thurs 12:00 to 1:30pm MCML 154

Office Hours: Thursdays 4-5pm

Office: MCML 352

Computer Lab: Thursdays 4-5pm. Generally speaking (except for the first 3 weeks of classes – please see detailed schedule on last page), the Computer Lab is optional and will take place at the discretion of the instructor to support student learning. Computer Labs held in MCML 154.

Course Support: Juan Fercovic, Academic Coordinator, MFRE Program


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.

Learning Outcomes

At the end of this course, students will be able to:

  • 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 application and use of Logistic Regression and Panel Data Analysis (specifically First Difference and Fixed Effects estimation).
  • 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.
  • Demonstrate proficiency in the use and application of Stata and R for conducting econometric analysis; Excel for data manipulation and conducting classical statistical tests; Tableau Prep for data cleansing/manipulation and cleansing; and Tableau Desktop for visual analytics.
  • Interpret and understand 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.
  • Demonstrate proficiency in the development of econometric models to your own academic work and summer internship project.

Course Structure

The course will be delivered in-person during scheduled class times. Some content may be provided in recorded format to support student learning. Students are expected to attend and participate as required in all class activities. Absenteeism will affect your grades for participation and in-class TBL activities.

Learning Management System and Communication

  • Canvas ( will be used for course content delivery.
  • Canvas Notifications and Student email addresses will be used for communicating information and disseminating class materials. It is your responsibility to check your email and the Canvas course website frequently.

Software, Textbook, Materials etc.

Required Software: Excel, Tableau Desktop, Stata and R 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. It is recommended to purchase your own license for $48 USD online at:
  • R statistical software will be required to be used in this course for a minimum of one assignment.

Recommended Textbook/References:

  • R. Carter Hill, William E. Griffiths and Guay C. Lim, Principles of Econometrics, 2011(4th edition), 2017(5th edition).
  • 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.
  • Introductory Econometrics: A Modern. Approach, 2012, 5th Edition. Jeffrey M. Wooldridge.

Recommended Calculator:

Learning and Assessments


Assessment Type Assuming In-person Exams
Assignments 20%
Team Project* 20%
Participation and Team-based Learning (TBL) Activities 5%
Midterm Exam 25%
Final Exam 30%
Total 100%

*Includes a peer review from your team. Grades will be modified in accordance to your individual contribution to your team’s project.

Course Policy: Assignments and Exams

Makeup Exams and Late Assignments

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.


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.

Academic Misconduct

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: The integrity of academic work depends on the honesty of all those who work in this environment and the observance of accepted conventions. 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.


All learning materials of this course (videos, course handouts, lecture slides, assessments, etc.) are the intellectual property of the instructor or licensed to be used in this course by the copyright owner. Redistribution of these materials by any means without permission of the copyright holder(s) constitutes a breach of copyright and may lead to academic discipline and could be subject to legal action. Audio or video recording of classes are not permitted without the prior approval of the Instructor. Any lecture video and recordings are for the sole use of the instructor and for students currently enrolled in this class. In no case may the lecture video or recording (or other learning materials), or part of the recording be used by students or any other person for any other purpose, either personal or commercial. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.

Schedule* (*Tentative at best!)

Week Lecture Topics Readings
1 - Sept 5 Introduction to course and econometrics. Introduction to data

visualization using Tableau Desktop. Computer Lab 1: Review of

descriptive statistics; Excel Data Analysis Toolpak; Data types;

classification of variables.

2 - Sept 12 Tableau Desktop. Class survey. Inferential Statistics; Estimation

Computer Lab 2: Pivot Tables. Assignment #1 posted.

3 - Sept 19 Regression Basics: the simple linear regression model – Lectures 1

and 2. Computer Lab 3: Comparison of population parameters

using hypothesis testing.


Hill Chapt 1-5

4 - Sept 26 Regression Basics: interval estimation and hypothesis testing;

prediction; goodness of fit; interpretation and modeling issues.

Regression Case Study. Assignment #2 posted.

5 - Oct 3 Transformations; Functional Forms. Discussion of Assignment 2.
6 – Oct 10 Complete simple linear regression. Introduction to Multiple


7 – Oct 17 Multivariate Regression (Introduction to multivariate analysis)

Midterm Exam: Thurs. Oct 20th (12pm – 2pm)*

8 - Oct 24 Multiple Regression continued. Indicator Variables. Tests for validity.

Assignment #3 posted.

Hill Chapt 6-8
9 – Oct 31 Heteroskedasticity. Tests for misspecification and structural stability.
10 - Nov 7 Autocorrelation. Assignment #4 posted.

Nov. 10th – Class cancelled (Midterm Break Nov. 9-11).

11 - Nov 14 An introduction to Panel Data Analysis. First Difference and Fixed



Chapt HO

12 - Nov 21 Qualitative Dependent Variables (Logit, Probit, etc) Hill

Chapt 16

13 - Nov 28 Qualitative Dependent Variables.

Dec. 1st – Final class. Team Projects are due – Sharing and Learning Session.

*Please note the midterm exam will extend beyond regular class time to allow extra time.