RES-ECON 702: Econometric Methods

Example syllabus only – exact content subject to change. Please see your instructor’s syllabus for the current term for your specific course’s guidelines

Instructor: Jianjing Lin (Office: 306C Stockbridge Hall; Email: @email)
Lectures: TuTh 10:00AM – 11:15AM, in-person at Library Tower 1667-ComputerRm
Office hour: Tuesday 2:30PM – 4:00PM at 306C Stockbridge Hall or by appointment
Course website: Canvas

TA: Rajib Rahman (Email: @email)
TA office hour: Wednesday 2:00PM – 4:00PM at 410 Stockbridge Hall

Course description and objective

This course deals with econometric methods and applications that are commonly used in empirical microeconomic research. The main topics covered include least squares estimation, instrumental variables methods, panel data models, Generalized Method of Moments, and non-linear estimation models such as the Maximum Likelihood Estimator or methods for binary dependent variables. This course also includes hands- on experience with data analysis using Stata, illustrating how the techniques covered can be applied to real world problems.

Prerequisites

• Probability Theory and Statistical Inference (Res-Econ 701)

Learning outcomes

After completing the course, students will be able to

• Discuss key methods of econometric data analysis and the assumptions and benefits of each method
• Explain key mathematical concepts behind these methods, and conduct simple mathematical analysis or proof related to the key mathematical concepts
• Use the Stata statistical software to carry out kinds of econometric analysis covered in the course
• Evaluate the validity of econometric analysis in empirical studies
• Plan and execute independent research projects with econometric tools learned from this class

Useful references

• No required textbooks.
• Supplementary references:
  • William Greene, Econometric Analysis (used), Pearson.
  • James H. Stock, Mark W. Watson, Introduction to Econometrics (used), Pearson.
  • A. Colin Cameron, Pravin K. Trivedi, Microeconometrics Using Stata (used), Stata Press.

Required technology

• Stata
• Students can access Stata in class during lecture time or at the Learning Commons (the lower level of the Du Bois Library) outside lectures.
• Alternatively, students can purchase the student versions of Stata. More information can be found here: https://www.stata.com/order/new/edu/profplus/student-pricing/.
• Canvas
• All course materials—lecture slides, homework assignments, etc.— are on the course’s Canvas page.
• Echo360

All classes will be recorded and uploaded to Echo360: echo360.org.

Assessment

Final grades will be based on a curve, but the strictest possible grade thresholds are: A-90%, B-80%, and C-70%. I will calculate grades according to the following percentages:

• Attendance: I randomly take attendance for 12 times throughout the entire semester. The first two absence will be waived. After that, for each time of absence, the student will lose 10% of the attendance credit. If you expect you cannot attend a lecture, please DO email me BEFORE the lecture to avoid loss of attendance credits.
• Practice problems: There are 6-8 problem sets, usually after a section or an important topic. All problem sets are due at the beginning of class. Late submission will not be accepted. The lowest score in the problem sets will be dropped in calculating the final grades.
• Quizzes: Quizzes (close-book) will be assigned in class from time to time. Roughly 10 – 20 minutes will be devoted to the quiz, depending on the length and level of difficulty. Make-up quizzes are not allowed unless it is due to legitimate reasons AND the instructor is notified for absence in advance. The lowest score in the quizzes will be dropped in calculating the final grades.
• Two Midterm Exams: Tentatively scheduled during the regular lectures on March 5 and April 14.
• Final exam: Students are advised NOT to schedule long-distance travels until the final exam schedule is finally determined. Note that all exams are closed-book. Make-up examinations will NOT be administered except for extraordinary circumstances (accidents, family emergencies, UMass approved official activities, etc.), and will require proper documentation.
• Bonus? Yes!: You can get bonus points from active participation in class. How the bonus points are calculated into the final grade will be determined towards the end of the semester.

Important dates for this class

• February 19 (Thursday), no class (on Monday schedule)
• March 5 (Thursday), 1st Midterm exam
• March 16-20, Spring break – no classes
• April 14 (Tuesday), 2nd Midterm exam

Course topics and schedule 

(subject to change as the semester progresses)

• Week 1-2: Ordinary Least Squares
• Week 3: Standard errors and hypothesis teslng
• Week 4: General Least Squares
• Week 5-6: Endogeneity and instrumental variables
• Week 7-8: Fixed effects, random effects, and panel data
• Week 9: Generalized Method of Moments
• Week 10: Maximum Likelihood Eslmator
• Week 11: Discrete choice models

Diversity and inclusion

The University of Massachusetts Amherst is committed to policies that promote inclusiveness, social justice, and respect for all, regardless of race, color, religion, creed, gender, sexual orientation, age, national or ethnic origin, physical or mental disability, political belief or affiliation, marital status, veteran status, immigration status, gender identity and expression, genetic information, or any other characteristic or status protected by state or federal laws.

Academic Integrity Statement

UMass Amherst is strongly committed to academic integrity, which is defined as completing all academic work without cheating, lying, stealing, or receiving unauthorized assistance from any other person, or using any source of information not appropriately authorized or attributed. As a community, we hold each other accountable and support each other’s knowledge and understanding of academic integrity. Academic dishonesty is prohibited in all programs of the University and includes but is not limited to: Cheating, fabrication, plagiarism, lying, and facilitating dishonesty, via analogue and digital means. Sanctions may be imposed on any student who has committed or participated in an academic integrity infraction. Any person who has reason to believe that a student has committed an academic integrity

infraction should bring such information to the attention of the appropriate course instructor as soon as possible. All students at the University of Massachusetts Amherst have read and acknowledged the Commitment to Academic Integrity and are knowingly responsible for completing all work with integrity and in accordance with the policy (https://www.umass.edu/senate/book/academic-integrity-policy).

Accommodation Statement

The University of Massachusetts Amherst is committed to making reasonable, effective, and appropriate accommodations to meet the needs of students with disabilities and help create a barrier-free campus. If you have a disability and require accommodations, please register with Disability Services, meet with an Access Coordinator in Disability Services, and send your accommodation letter to your faculty. Information on services and registration is available on the Disability Services website (https://www.umass.edu/disability-services/).

Artificial Intelligence (AI)

This course assumes that all work submitted by students will be generated by the students themselves, working individually or in groups. Students should not have another person/entity do the writing of any substantive portion of an assignment for them, which includes hiring a person or a company to write assignments and using artificial intelligence tools like Copilot, ChatGPT and Google Gemini. The assignments in this class are designed to support your learning and development of critical thinking skills. The use of AI may limit your success in meeting the course learning outcomes.

Title IX Statement

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