ECON 4014 - Econometrics IV (H)

North Terrace Campus - Semester 1 - 2023

The objective of this course is to study more advanced topics in econometrics. Students are expected to have knowledge in statistics and multiple regression models at the level of Econometrics III/PG or equivalent. Topics typically include linear regression models, instrument variables (IV) estimation, generalized method of moment (GMM), maximum likelihood estimation (MLE), limited dependent variable (LDV) models, treatment effect and sample selection corrections, panel data methods, Monte Carlo simulations and bootstrap methods. The emphasis is on understanding the models and the related theories. Through the course, we will apply the theories developed to real-world data and interpret the estimation results in many different respects.

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Course Details

Course Code	ECON 4014
Course	Econometrics IV (H)
Coordinating Unit	Economics
Term	Semester 1
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 4 hours per week
Available for Study Abroad and Exchange	N
Prerequisites	A minimum of a Credit in ECON 3502
Incompatible	ECON 7204
Restrictions	Available only to students enrolled in the Bachelor of Economics (Honours) program
Assessment	Typically homework problem sets, Just in time teaching (JiTT), empirical projects & final exam

Course Staff

Course Coordinator: Professor Firmin Doko Tchatoka

Office location: Nexus 10, Level 4, Room 4.47
Telephone: 8313 1174

Course Timetable

The full timetable of all activities for this course can be accessed from .

The detailed list of topics will be given in the first class and posted on MyUni.

Course Learning Outcomes

On successful completion of this course students will be able to:

1	Acquire knowledge of various advanced econometric models, estimation methods and related econometric theories
2	Apply the above theories to empirical data or be able to develop new econometric theory
3	Write Matlab code and how to use statistical packages like STATA to estimate econometric models using real world data
4	Work in groups when doing problem solving and computer exercises, and present relevant research papers in the field of applied or theoretical econometrics
5	Conduct econometric analysis of data properly and understand the results

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

University Graduate Attribute	Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.	1,2,3,4
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.	2,3,5
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.	4,5
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.	4,5
Attribute 5: Intercultural and ethical competency Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.	4
Attribute 8: Self-awareness and emotional intelligence Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.	1,4,5

Required Resources

Lecture notes will be posted on MyUni before each lecture.

Required Textbook


Marno Verbeeck		A Guide to Modern Econometrics		4th Edition, A John Willey & Sons, Ltd, 2012

Computer Software

1	Matlab	Available on the computers in Honours student room, PhD student room, and the computer lab (10 Pulteney St. 2.20 Computer Suite 1 and Computer Suite 3)

2	Stata	Available on the computers in Honours student room, PhD student room, and the computer lab (10 Pulteney St. 2.20 Computer Suite 3 only)

NB: Students are encouraged to use software other than the ones listed here. However, they must ensure that the software is appropriate for their project. Students who use computers connected to the University network can request ITS to install Matlab on their machines.

Recommended Resources


A.C. Cameron and P.K. Travedi	Microeconometrics: Methods and Applications	Cambridge University Press, 2005
J.M. Wooldridge	Econometric Analysis of Cross Section and Panel Data	MIT Press, 2002
F. Hayashi	Econometrics	Princeton University Press, 2000
P. A. Ruud	An Introduction to Classical Econometric Theory	Oxford, 2000.
J. Hamilton	Time Series Analysis	Princeton University Press, 1994

Online Learning


1	E-mail	Check your student email often as course-related announcements are communicated via email
2	MyUni	All the materials such as lecture notes, problem sets and their answer keys, Matlab manual, etc. will be posted on the MyUni course webpage,

NB: Lecture notes will be put on the course webpage before each lecture. Students need to print out lecture notes and bring them to class.

Learning & Teaching Modes

1	Lecture notes
2	Reading textbooks
3	Just in time teaching (JiTT) assessment
4	Problem solving and computer exercises
5	Empirical Project

NB: It is important for students to be able to apply what they learn in class to real world data by using computer programs such as Matlab, Gauss, C++, R and Stata.

Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

Any student in this course is expected to attend all lectures, workshops and labs throughout the semester.

Lecture notes		2 hours/week
JiTT		3 hours/week
Additional readings and empirical project		2 hours/week
Problem solving and computer exercises		2 hours/week

NB: The above guide is for private study, that is, study outside of your regular classes.

Learning Activities Summary

Tentative Course Schedule (subject to changes)

1		Review of the classical multivariate linear model: estimation, inference, and violation of basic assumptions
2		Instrumental variables methods and GMM
3		Nonlinear least squares (NLS) and Maximum likelihood (ML) estimations
4		Models With Limited Dependent Variables
5		Models Based on Panel data
6		Sample Selection and Treatment Effects

Specific Course Requirements

N/A

The University's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

The final mark for this course will be determined by:

Assessment Task	Task Type	Due	Weighting	Learning Outcome
Just in Time Teaching (JiTT)	Readings	Refer to MyUni	10%	1-5
Problem sets and computer exercise	Problem solving and computing	Refer to MyUni	30%	1-5
Empirical project	Formative, reading and computing	Refer to MyUni	20%	1-5
Final exam	Formative, problem solving and computer exercises	Refer to MyUni	40%	1-5

Assessment Related Requirements

N/A

Assessment Detail

1. Just in Time Teaching (JiTT)

In the unit I plan to use the Just in Time Teaching (JITT) technique. You will be required to read some material before the relevant workshop and lecture. I will post the questions on MyUni. There will be three questions that will be covered in the following week’s lecture, workshops and labs. You will submit your answers by Saturday 5pm. It is important to bear in mind that while you will not be assessed on the content of your answers I will nevertheless use the JiTT assessments to form a question in the midterm and final exams. I will also form view of the effort you are putting into being prepared for the following week’s class—I read your submissions before the Monday class. The mark here is an incentive to encourage you to participate rather than an assessment of the content.

2. Homework and Computer Exercises

Problem sets and computer exercises will be given to you fortnightly. Details (including submission dates) will be provided on MyUni and discussed with students in lectures. Late submission will be accepted only if accompanied by appropriate documentation, for example, a medical certificate. Each student must write and turn in her/his own homework to me right before lecture begins in class on the due date. Students must write their name and student ID number on the cover sheet.

3. Empirical Project

Students must complete an applied econometric study of an economic or financial relationship and answer a research question that they pose. The maximum length of the final version of the project is 15 pages + references + appendix. Students must replicate one of the following paper:

1. Fama, E. and French, K., 2004. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, (33) 3-56.

2. I. Mourife and A. Siow, 2000. The Cobb Douglas Marriage Matching Function: Marriage matching with peer effects, 2021. Journal of Labor Economics.

3. Daron Acemoglu, Simon Johnson, James A. Robinson, 2001. The Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review, 91(5) 1369-1401.

4. Graddy, K., 1995. Testing for Imperfect Competition at the Fulton Fish Market. The RAND Journal of Economics, (26) 75-92.

5. Narayan, P., Narayan, S., and Prasard, A., 2008. Understanding the Oil Price-exchange Rate Nexus for the Fiji Islands. Energy Economics (30) 2686-2696.

6. Nunn, Nathan, and Leonard Wantchekon, 2011. The Slave Trade and the Origins of Mistrust in Africa. American Economic Review, 101(7) 3221–3252.

7. Esther Duflo, Pascaline Dupas and Michael Kremer, 2015. Education, HIV and Early Fertility: Experimental Evidence from Kenya.
American Economic Review, 105(9), pp. 2257-97.

8. Susan Athey, Jonathan Levin and Enrique Seira, 2011. Comparing Open and Sealed Bid Auctions: Theory and Evidence from Timber Auctions. Quarterly Journal of Economics, vol. 126(1), 207-257.

The project is divided in three parts: A, B, and C.

Project Part A (due date: Week four of March): Must contain the abstract and data description

Abstract:

1. Clearly state the question that you will be investigating. Do not repeat the abstract from the source paper.

2. Provide the source of the data or the name of the database that you are planning to use.

3. Speculate what type of results you would expect to get in answer to your stated question.

Expected length: 1 page

Data Description:

4. Type of data (e.g. panel, time series, cross sectional, pooled cross sectional, etc.).

5. Frequency.

6. Dimensions of your data.

7. List variables that you will be using for your project.

8. Provide 5-point summary for all variables used in your analysis.

9. Graph your data and interpret the results (stationarity, seasonality, trends, structural breaks…).

Expected length: 3-4 pages

Project Part B (due date: Week four of April): Residual Analysis

In this part of the assessment you have to specify and test the data selected for your project with your chosen models. Justify the models’ selection through residual analysis and additional tests (you will have to determine which tests will be applicable for your chosen data type and chosen models).

Expected length: 2-3 pages

Project Part C (due date: week two of June): Final Empirical Project

The term paper is your opportunity to construct a model and analyze it using econometric methods. A good paper will have the following format structure:

1. Introduction (modified and improved Project Part A)

a. Why do we care?

b. What else is known about this problem?

c. What are the limitations of previous studies?

2. Data

a. Data collection

b. Sources and Descriptive statistics (modified and improved Project Part A)

3. The model

a. Estimation and Testing

b. Residual analysis (modified and improved Project Part B)

4. Results

a. What are the main findings?

b. Do you find the empirical results convincing?

c. Interpret your findings and stress their significance

5. Conclusion

a. Summary of main contributions

b. How do you think the study could be improved?

6. Reference List

Each term paper will have an assignment submission cover page. Projects should be up to 15 pages of text (with all references cited in the appropriate text), bibliography, tables and figures, and any appendix material. You must include all relevant computer printouts including one that clearly lists your data in a compact. Your grade will depend on your mastery of the relevant econometric theory and the organization of your paper.

4. Final Exam

2 hours multi-part problem solving questions: will cover all the lectures, JiTT, Homework and Computer Exercises, and labs. Written sample answers will not be provided. Help with questions that you have made a genuine attempt to answer may be provided by your lecturer/tutor either individually or in a group.

Submission

Refer to ASSESSMENT DETAIL. After being marked, generally, the assessment will be returned to students in class about a week after
submission.

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M11 (Honours Mark Scheme)
Grade	Grade reflects following criteria for allocation of grade	Reported on Official Transcript
Fail	A mark between 1-49	F
Third Class	A mark between 50-59	3
Second Class Div B	A mark between 60-69	2B
Second Class Div A	A mark between 70-79	2A
First Class	A mark between 80-100	1
Result Pending	An interim result	RP
Continuing	Continuing	CN

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through .

Additional Assessment
If a student receives 45-49 for their final mark for the course they will automatically be granted an additional assessment. This will most likely be in the form of a new exam (Additional Assessment) and will have the same weight as the original exam unless an alternative requirement (for example a hurdle requirement) is stated in this semester’s Course Outline. If, after replacing the original exam mark with the new exam mark, it is calculated that the student has passed the course, they will receive 50 Pass as their final result for the course (no higher) but if the calculation totals less than 50, their grade will be Fail and the higher of the original mark or the mark following the Additional Assessment will be recorded as the final result.

Student Feedback

The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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