Course:FRE529/Syllabus

COURSE INFORMATION

Class Time: Tuesdays and Thursdays 12:30 to 2:00pm Room: MCML 154

Jan 8 – Feb 16th, 2024

Instructor:

Michael Johnson

Contact Info: Email: mjohnson@mail.ubc.ca

Office Hours: Tues 3:50-4:50pm (MCML 352)

Course Support:

Juan Fercovic

Email: juan.fercovic@ubc.ca

Office Hours: TBA (See Canvas front page)

Prerequisite

FRE528 Applied Econometrics

COURSE OVERVIEW

This course covers advanced econometric methods and related econometric theories useful for economists working in the food and resource sectors. Topics can include instrument variables (IV) estimation, experiments and quasi-experiments (difference-in-difference estimation) and panel data methods (basic models and dynamic panel models). The focus of the course will be on the application of these methods in econometric modeling rather than on theoretical proofs.

LEARNING OUTCOMES

• To learn various advanced econometric methods, estimation methods and related econometric theories.
• To apply these methods to data or econometric modelling techniques to estimate models using real world data.
• To be able to write a code in R and Stata to estimate econometric models and replicate results from published econometrics research.
• To be capable of interpreting econometric estimates, analyzing the results and critically evaluating published econometric research that use such econometrics methods.
• To be able to formulate your own research question based on a given journal paper and data availability. To develop a small original research study that is an extension of a current research paper in the area of food and resource economics.
• To use and apply econometric methods in the development of their own research study (that may take place during the summer internship projects).

GOALS OF THIS COURSE

The purpose of this course is to provide an understanding of some of the more important econometric models and applications that will benefit the MFRE students in a future professional or academic role. This course will focus on three important areas of econometrics: IV estimation, Experiments and Quasi-Experiments and Panel Data Methods. As stated by Angrist and Pischke (2009), in their book Mostly Harmless Econometrics: An Empiricist's Companion, some of the most important items in an applied econometrician’s toolkit are:

1. Regression models designed to control for variables that mask the causal effects of interest;
2. Instrument variables methods for the analysis of real and natural experiments; and
3. Difference-in-difference-type strategies that control for unobserved omitted factors.

COURSE ASSESSMENT

Evaluation	Date	Percent of Grade
Midterm Exam	Tues Feb 6th during class	30 percent
Assignments	Two Assignments (expected* due dates): Assignment 1 – Jan 31 Assignment 2 – Feb 16	30 percent
Team Final Project	Preliminary Results Due: Fri Jan 26 Final Project Due: Wed Feb 14	30 percent
Class Participation	Contributions to class discussions.	10 percent

*please see Canvas for exact due dates. These dates are estimated only and may change.

Team-based learning will be applied in this course. The class participation grade depends on your contribution to class discussions and the overall learning environment. The sole aim of assigning a participation grade is to encourage active learning for everyone.

The assignments will consist of an applied problem that will allow you to apply the various techniques and topics covered in class using real data sets. In addition, you will get practice in the use of both R and Stata.

Stata: An educational version of Stata (Stata/BE) can be purchased for $48 USD from: https://www.stata.com/order/new/edu/profplus/student-pricing/

A Team Final Project will be assigned based on the content and coverage of the models discussed. Details of the team project will be discussed in class and will be posted in Canvas under Assignments. A heavy penalty will be applied to late assignments/final project unless there is a medical or extenuating circumstance.

There will be NO makeup exams. 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 (not after the exam or deadline has taken place). Notification after the examination date is not acceptable.

Textbook and Resources:

A variety of journal publications (links provided later) will be discussed during class to strengthen a deeper understanding of the econometric models and its application to food and resource economics. Course materials will be drawn from a variety of textbooks:

• Wooldridge, Jeffrey M., Basic Linear Unobserved Effects Panel Data Models (Chapter 10), Econometric Analysis of Cross Section and Panel Data, 2002.
• R. Carter Hill, William E. Griffiths and Guay C. Lim, Principles of Econometrics (Chapters 10, 15 and 16), Fourth Edition, Wiley, 2011.
• James H Stock and Mark Watson, Introduction to Econometrics (Chapters 10, 11, 12 and 13), 3/E, Pearson (2012).
• Verbeek, M. (2012). A Modern Guide to Econometrics, Fourth Edition. John Wiley & Sons.
• Joshua D. Angrist and Jörn-Steffen Pischke, Mastering 'Metrics: The Path from Cause to Effect, Princeton University Press, 2015.
• Joshua D. Angrist and Jörn-Steffen Pischke, Mostly Harmless Econometrics: An Empiricist's Companion, Princeton University Press, 2009.

COURSE SCHEDULE

Week 1 Jan 8	Course Introduction. Endogeneity and start of IV Estimation.
Week 2 Jan 15	IV estimation (Applications to The Causal Effect of Studying on Academic Performance; Agricultural Supply and Demand; Consumerism application).	Assignment 1 posted
Week 3 Jan 22	IV estimation (continued). Hands-on exercise using IV estimation (wages and education)
Week 4 Jan 29	Review of Fixed Effects Estimation from FRE 528 (First Differences and Fixed Effect Estimation (applications: training, product quality, real estate price estimation, policies relating to taxation and driving laws). Panel Data assumptions. Panel Data: Random Effects Models. Summary of panel methods/hands-on exercise.	Assignment 2 posted
Week 5 Feb 5	Midterm Exam (30%) - Tues Feb 6th Experiments and quasi-experiments. Difference in difference estimation (waste disposal application).
Week 6 Feb 12	Threats to Validity of Experiments. Team Final Project (30%) Due – Wed Feb 14th

Please Note: This is a tentative Lecture Schedule that may change.

ACADEMIC HONESTY POLICIES

Plagiarism and Academic Dishonesty

Academic dishonesty and plagiarism are taken very seriously in the MFRE program. All incidences of plagiarism will be escalated to the MFRE Academic Director with penalties ranging from a mark of zero on the assignment, exam or course to being required to withdraw from the program. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.

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.
• Using Generative Artificial Intelligence (AI) tools like ChatGPT, Bard, or other Generative AI models to generate content or conduct analysis for evaluations, without proper citation and or if asked not to use AI, is considered plagiarism and academic misconduct. If students use AI in their submissions, they must cite the AI generator using citations consistent with the UBC Academic Honesty Standards.
• 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.
• Working with Others on an Assignment: You are encouraged to work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, this will be considered academic dishonesty and this will be handled according to the MFRE and UBC policies.
• 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 MFRE Code of Conduct; MFRE Turn it in, Course Syllabus, MFRE Instructors; Canvas and UBC academic misconduct policies.

Penalties for Academic Dishonesty: Penalties for academic dishonesty are applied at the discretion of the MFRE program. Incidences of academic misconduct may result in a mark of zero on the assignment, examination, or course, required withdrawal from the program, and/or the matter being is referred to UBC Graduate Studies.