|Estimating Econometric Models|
|Instructor:||Dr. Michael Johnson|
|Office Hours:||10-11 Wed|
|Class Schedule:||Feb 28 to Apr 8
Tues and Thurs 12:30 to 2:00pm
|Important Course Pages|
Michael Johnson, PhD
Office Hours: Wed 10-11am
Office Hours: TBA
FRE528 Applied Econometrics
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.
- 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 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 use and apply these 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:
- Regression models designed to control for variables that mask the causal effects of interest;
- Instrument variables methods for the analysis of real and natural experiments; and
- Difference-in-difference-type strategies that control for unobserved omitted factors.
Your grade shall be determined as follows:
|Evaluation||Date||Percent of grade|
|Midterm Exam||TBA||30 percent|
|Assignments||Two Assignments||30 percent|
|Team Final Project||Due at the end of the course||30 percent|
|Class Participation||Contributions to class discussions.||10 percent|
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 the students to apply the various techniques and topics covered in class using real data sets. In addition, they will get practice in the use of the statistical software R and Stata.
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. A heavy penalty will be applied to late assignments/final project unless there is a medical or extenuating circumstance.
Academic dishonesty and plagiarism are taken very seriously in the MFRE program and can result in a range of punitive measures, which could include failing the program. It is each student’s responsibility to review and understand what constitutes academic dishonesty and plagiarism and how to avoid them.
Academic honesty is essential to the continued functioning of UBC 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:
- UBC Academic Misconduct and Discipline (http://www.calendar.ubc.ca/Vancouver/index.cfm?tree=3,54,111,0)
- UBC Learning Commons web-based Academic Integrity (http://learningcommons.ubc.ca/academic-integrity/).
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.
Online Course Material
All materials will be posted through Canvas.
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.
- 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. 2011.
- 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.
Tentative Lecture Schedule
Please Note: This is a tentative Lecture Schedule that may change.
|Week 1||Course Introduction; Endogeneity and start of IV Estimation.|
|Week 2||IV estimation (Applications to The Causal Effect of Studying on Academic Performance; Agricultural Supply and Demand;
|Week 3||IV estimation (continued).
Hands-on exercise using IV estimation (wages and education)
|Week 4||Introduction to Panel Methods. First Differences Estimation (Application to training and product quality).
Panel Data – Fixed Effects Estimation (Policies relating to taxation and driving laws; Voting bias in sporting competitions).
|Week 5||Midterm Exam (30%)
Panel Data assumptions. Panel Data: Random Effects Models.
Summary of panel methods/hands-on exercise.
|Week 6||Experiments and quasi-experiments. Difference in difference estimation (waste disposal application).
Threats to Validity of Experiments.
Team Final Project (30%) – Thurs April 7th