Course:FRE528

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Applied Econometrics
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FRE 528
Section:
Instructor: Michael Johnson, PhD
Email: mjohnson@mail.ubc.ca
Office: MCML 352
Office Hours: Thurs 4:30-5:30 pm
Class Schedule: Tues and Thurs 11:30-1:00pm
Classroom: MCML 154
Important Course Pages
Syllabus
Lecture Notes
Assignments
Course Discussion


Course Information

Instructor: Michael Johnson, PhD

Lectures: Tues and Thurs 11:30 to 1:00pm in MCML 154 (with some modification – please see the weekly MFRE online schedule and the class schedule posted on page 6 and 7)

Office Hours: Thursdays 4:30-5:30pm Office: MCML 352

Office: MCML 352

Computer Lab: Thursdays 4:30-5:30pm. Go under the assumption of NO computer lab unless announced by the instructor during lecture. Computer labs only take place when required to support student learning – more so to support learning of software. Computer Labs held in MCML 154. On days of no computer lab, Mike will attend his office hours in MCML 352.

Course Support: Juan Fercovic, Educational Researcher, MFRE Program

Email: juan.fercovic@ubc.ca (Please see Canvas for Office Hours)

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-based project 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 (http://canvas.ubc.ca) 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, and R will be used in this course.

  • R statistical software will be required to be used in this course for a minimum of one assignment. Please be sure to download and install the most recent versions of R and R Studio for the course. Download R: Visit: https://www.r-project.org/ Once you have downloaded R, you can download RStudio. Download RStudio: Visit: https://rstudio.com/products/rstudio/download/
  • Please signup free your free student copy of Tableau Desktop using the following link: https://www.tableau.com/academic/students. Be sure to sign up for the software using your UBC email account. We will also be using Tableau Prep for data cleansing, manipulation and preparation.

Recommended Textbook/References:

  • R. Carter Hill, William E. Griffiths and Guay C. Lim, Principles of Econometrics, 2011 (4th edition), 2017 (5th edition). Available in KOERNER LIBRARY reserve collection: http://resolve.library.ubc.ca/cgi-bin/catsearch?bid=4897188
  • Introductory Econometrics: A Modern. Approach, 2012, 5th Edition. Jeffrey M. Wooldridge. (Chapters 13 and 14 on Panel Data Analysis)
  • Several econometrics papers will be provided via Canvas for additional reading

Recommended Calculator:

  • Any 2-variable scientific calculator (Sharp EL 531 or equivalent) is required. The following is available through the UBC bookstore:https://the.bookstore.ubc.ca/Item?item=3425821 Lectures on regression analysis will be taught using the Sharp EL 531. Programmable calculators are not allowed during exams.

Learning and Assessments

Evaluation

Assessment Type Assuming In-person Exams
Assignments 20%
Replication Project* 15%
Participation and Team-based Learning (TBL) Activities 5%
Midterm Exam 25%
Final Exam 35%
Total 100%

*Teams of 4 students. Details of the Replication Project will be discussed during class.

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.

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. If you are unable to attend class due to illness, it is your responsibility to inform the instructor prior to class.

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.

Copyright

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.

AI Policy

Students are permitted to use artificial intelligence tools, including generative AI, to gather information, review concepts or to help produce assignments and the replication project. However, students are ultimately accountable for the work they submit (they can be wrong!), and any content generated or supported by an artificial intelligence tool must be cited appropriately (see: https://apastyle.apa.org/blog/how-to-cite-chatgpt). Note: when referencing the use of gen AI tools on assignments, it is expected that you provide both your prompt and the output that was provided by the gen AI tool so your instructor can ascertain the extent of your own original content. Please read the section on Academic Policy that discusses plagiarism. NOTE: Use of AI tools is NOT permitted during quizzes, midterm exams and final exams in this course.

Schedule* (*Tentative at best!)

Week Lecture Topics Readings
1 - Sept 2 Tues

Introduction to course and econometrics. Wed Introduction to data visualization using Tableau Desktop.

Assignment #1 posted.

Thur

Tableau Desktop and Tableau Prep. Visual analytics; Clustering Analysis.

Handouts

provided; Hill Chapts1-2.

2 - Sept 9 Tues

Statistics Inquiry. (t Tests reviewed!).Introduction of simple linear regression (SLR1). Assumptions, Estimation and Fit

Wed

Complete Introduction to SLR1.Assignment #2 posted

Thur

Simple linear regression (SLR2). Interval estimation, prediction; interpretation and modeling issues.

3 - Sept 16 Tues

Completion of last day’s content (SLR2). Wed Tests of significance (SLR3). Assignment 2 discussion.

Thur

Introduction to functional forms; Case Study.

Handouts

Hill Chapt 3-5

4 - Sept 23 Mon

Functional forms; transformations.

Tues

Assignment discussion.

Thur –No class

5 - Sept 30 Tues

Introduction to Multiple Regression

Thurs Oct 3

Midterm Exam*: Thurs Oct3rd(11:30am–1:30pm)

6 – Oct 7 Tues

Multiple Regression continued (Backwards-PI). Assumptions. Stepwise processes and model development.

Thur

Multiple Regression continued (Backwards-PII). Diagnostic tests.

Assignment #3 posted

7 – Oct 14 Tues

Indicator Variables for categorical data. Coding, prediction, interpretation

Thur

Heteroskedasticity.

Hill Chapt 6-8
Weeks 8-10 During Weeks 8 to 10 there will be no scheduled classes as per the MFRE schedule!
11 - Nov 11 Midterm Break Nov. 11-13 (No class Tues Nov. 12)

Thur

Tests for misspecification & structural stability.

Assignment #4posted.

12 - Nov 18 Tues

Autocorrelation.Tests and remedies.

Wed

Introduction to Panel Data Analysis. First Differences.

Thur

Fixed Effects (within estimator).

Wooldridge

Chapt HO

13 - Nov 25 Tues

Introduction to Logistic regression; probit.

Thurs

Logistic regression; diagnostics, prediction, model accuracy and assessment.

Fri

Other models dealing with binary/nominal dependent variables.

Diagnostics and applications.

Hill

Chapt 16

14 - Dec 2 Dec.3/5.Team Project Presentations.

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

Final Exam–Scheduled by MFRE Admin

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