ECON 7204 - Econometrics IV

North Terrace Campus - Semester 1 - 2014

The objective of this course is to study more advanced topics on micro-econometrics. Students are expected to have knowledge in calculus, statistics and multiple regression models at the level of Econometrics III/IIID or equivalent. Topics include linear regression model, some topics on regression, bootstrap, generalized method of moment (GMM), empirical likelihood (EL), instrument variables (IV) estimation, panel data methods, simultaneous equation models, limited dependent variable models, and sample selection corrections. The emphasis is on understanding the models and theories. Through the course, we will also apply econometric estimation methods to real-world data and interpret the estimation results in many different respects.

  • General Course Information
    Course Details
    Course Code ECON 7204
    Course Econometrics IV
    Coordinating Unit Economics
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Prerequisites A minimum of a Credit in ECON 3023 or ECON 3507 or ECON 7022 or equivalent
    Assessment Typically, homework, paper & final exam
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    Office location: Nexus 10, Level 4, Room 4.05
    Telephone: 8313 4821
    Course Timetable

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

    The detailed list of topics will be given in the first class and posted on MyUni.
  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

    1 To acquire knowledge of various advanced econometric models, estimation methods and related econometric theories
    2 To learn how to apply the above theories to empirical data or be able to develop new econometric theory
    3 To learn how to write Matlab code and how to use statistical packages like STATA to estimate econometric models usng real world data
    4 To be able to work in groups when doing problem solving and computer exercises, and present relevant research papers in the field of applied or theoretical econometrics
    5 To be able to 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)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1, 2
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 4
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2, 3
    Skills of a high order in interpersonal understanding, teamwork and communication. 4
    A proficiency in the appropriate use of contemporary technologies. 2
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 5
  • Learning Resources
    Required Resources
    Required Textbook:
    W. H. Greene, Econometric Analysis, 5th Edition, Prentice Hall, 2003

    Computer Software:
    There will be about 6 problem sets. The assignments have both problem solving questions and computer exercises. The software that will be used for computation for this course is Matlab and Stata. These software are available on the computers at Nexus 10 Tower, Room 2.28 and Honours room. Students who use computers connected to the University network can make a request to the ITS to install Matlab in their machines.
    Recommended Resources

    W. H. Greene, Econometric Analysis, 5th Edition, Prentice Hall, 2003.

    J. M. Wooldridge, Introductory Econometrics, 4th Edition, South-Western, 2009

    J.M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, MIT Press, 2002

    P. Kennedy, A guide to Econometrics, 5th Edition, MIT Press, 2003.

    J. Davidson, Econometric Theory, Blackwell Publishing, 2000.

    P. A. Ruud, An Introduction to Classical Econometric Theory, Oxford, 2000.

    R.C. Hill, W. Griffiths and G.G. Judge, Learning and Practicing Econometrics, Wiley, 1993.

    R. Davidson and J. G. MacKinnon, Econometric Theory and Methods, Oxford University Press,


    Probability and Statistics:

    R.V. Hogg, A. Craig and J.W. Mckean, Introduction to Mathematical Statistics 6th Edition, Prentice

    Hall 2005.

    M. H. Degroot and M. J. Schervish, Probability and Statistics, 3rd Edition, Addison-Wesley 2002
    Online Learning
    Lecture materials may be posted on MyUni. But lectures will not be recorded. Please check the 
    course page frequently for important announcements and corrections.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The material will be given during class times. Students are expected to work on their homework weekly to practice the material and learn applications. At the end of the class students will present applied econometrics papers using the methodology covered in class. Students are allowed to work in groups and discuss problem sets. However, the homework submitted for assessment must be individual work.

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

    For the first half of the semester, weekly problem sets might be assigned. Some homework questions are not for assessment but students are expected to attempt each exercise. The homework sets that are graded will be announced in class and on myUni. Towards the end of semester, students will be given a list of articles to choose from for their presentations, which are assessable. There will be a take-home final test.
    Learning Activities Summary
    Possible list of topics (Tentative and Subject to time availability):

    1. Review of Probability and Matrix Algebra

    2. Restricted Least Squares, Projection Algebra, Partial Regression

    3. Classical Linear Model (Nonstochastic Regressors) Gauss-Markov Theorem and OLS, Violation of Homoscedasticity: GLS

    4. Classical Linear Model with Normal Errors Multivariate Normal Distribution, Sampling Distribution of OLS, Confidence Intervals and Hypothesis testing, Testing Restrictions. Haustman, MOM Interpretation of IV, Endogenous Regressor, SEM, Identification and 2SLS Estimation, panel data and fixed effects

    5. Limited Dependent Variable Binary Response Model: Probit, Logit, Truncation and Censoring

    6. GMM

    7. Panel Data, Diffs-in-diffs, Fixed Effects, Random Effects, First Difference, Dynamic Panel Data: difference and system GMM, Arellano and Bond, Collapsed instruments set and number of instruments problem
  • Assessment

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

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

    Assessment Summary
    The grading scheme is as follows:

    Presentation 20%
    Homework 40%
    Final examination 40%
    Assessment Related Requirements
    Students are required to attend all classes and do all homework exercises. Unless otherwise stated, students are encouraged to work in groups and discuss the work together. However, you have to write up your own answers individually, when submitting your work for a grade.
    Assessment Detail
    The assessment will consist of homeworks, a take-home final test, and a class presentation of an applied paper. The schedule for the class presentations as well as the list of papers that students can choose for the presentation will be announced in due course.
    The deadlines for submission will be announced in class and posted on myUni. No late work will be accepted. If you are unable to submit work on time due to special reasonable circumstances, please talk to me before the due date. There will be no supplementary finals or homework.
    Course Grading

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

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    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 Access Adelaide.

  • 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 ( 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.

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  • Policies & Guidelines
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