ECON 7051 - Intermediate Econometrics IID

North Terrace Campus - Semester 1 - 2017

This course provides an introduction to the econometric techniques used to analyse data sets in economics, business and finance. It builds on basic statistics, inference and regression as covered in introductory statistics courses, but does not include time series econometrics. The focus is on understanding the methods involved, using statistical software to provide the results and then interpreting and commenting on these results. The course reviews basic statistics, regression and inference, and then introduces multiple regression analysis, which remains the most commonly used statistical technique in econometrics. The remainder of the course considers various practical aspects of linear regression models and may include dummy variables, different functional forms and the consequences of violation of the classical regression assumptions

  • General Course Information
    Course Details
    Course Code ECON 7051
    Course Intermediate Econometrics IID
    Coordinating Unit School of Economics
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Assumed Knowledge Introductory Statistics, Microeconomics & Macroeconomics
    Restrictions Available to MFin&BusEc, GCertAppEc, GCertIntEc, GDipIntEc, GDipAppEc, MAppEc & MHlthEco&Pol students only
    Course Description This course provides an introduction to the econometric techniques used to analyse data sets in economics, business and finance. It builds on basic statistics, inference and regression as covered in introductory statistics courses, but does not include time series econometrics. The focus is on understanding the methods involved, using statistical software to provide the results and then interpreting and commenting on these results. The course reviews basic statistics, regression and inference, and then introduces multiple regression analysis, which remains the most commonly used statistical technique in econometrics. The remainder of the course considers various practical aspects of linear regression models and may include dummy variables, different functional forms and the consequences of violation of the classical regression assumptions
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    Semester 1
    Course Coordinator: Dr Nadya Baryshnikova
    Email: nadya.baryshnikova@adelaide.edu.au
    Office hours: TBA
    Office location: Nexus 10, Level 4, Room 4.04
    Telephone: 8313 4821

    Semester 2
    Course Coordinator: Dr Patricia Sourdin
    Office hours: TBA
    Office location: TBA
    Telephone: TBA

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

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

    1. Conduct basic statistical and econometric analysis.
    2. Explain and interpret econometric results.
    3. Formulate a research question and answer it using basic econometric analysis.
    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1,2,3
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    1,2,3
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1,2,3
  • Learning Resources
    Required Resources

    TEXT BOOK(S)
    The required textbook is Principles of Econometrics, 4th Edition, Wiley by R. Carter Hill, William E. Griffits and Guay C. Lim.

    Recommended Resources

    The recommended book to accompany the required text is Using Stata for Principles of Econometrics, 4th Edition, by Adkins and Hill.

    Online Learning

    MyUni Course WebPage provides lecture notes and other course materials. Please check this page frequently for important announcements and corrections.

  • Learning & Teaching Activities
    Learning & Teaching Modes

    Classes will meet three times a week: 2 hours for lecture and once for a 1-hour tutorial. Office hours will be announced in due course. Please adhere strictly to the designated office hours.

    Workload

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

    The standard undergraduate workload for a full-time student is 48 hours per week which equates to 12 hours per 3 unit course. This course has three hours of lectures and tutorials each week, which means that students should undertake nine hours of self-study each week of the teaching term.

    Note that postgraduate students are expected to perform additional tasks (these may be computational in nature) during tutorial sessions.

    Homework assignments are issued each week. Students are expected to work in groups on these assignments. In addition to the weekly group questions there will be two assignments to be submitted individually for a grade. All students may be asked to present their solutions during each tutorial session.

    Learning Activities Summary
    Teaching & Learning Activities Related Learning Outcomes
    Lectures 1,2,3

    TENTATIVE LECTURE SCHEDULE (subject to change) :

    Topics Title Chapters
    Topic 1 Introduction to Econometrics Chapter 1 (HGL)
    Topic 2 Introduction to Basic Statistics and Probability Probability Primer
    Topic 3 The Simple Linear Regression Model Chapter 2
    Topic 4 Interval Estimation and Hypothesis Testing Chapter 3
    Topic 5 Prediction, Goodness of Fit and Modeling Issues Chapter 4
    Topic 6 The Multiple Linear Regression Model Chapter 5
    Topic 7 Further Inference in the Multiple Regression Model Chapter 6
    Topic 8 Using Indicator Variables Chapter 7
    Topic 9 Heteoskedasticity Chapter 8
    Specific Course Requirements

    Homework completion may require access to STATA. If you do not have STATA at home, you may use the computer labs on campus. Please refer to http://www.adelaide.edu.au/its/student_support/labs/ for further details.

    For course related questions, students are encouraged to utilise the designated office hours of the lecturer. Questions over the telephone are strongly discouraged.

  • 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
    Assessment Task Due Date/ Week Weight Learning Outcomes
    Homework Assignments (in groups) Week TBA 20% 1,2
    Individual Homework Assignments Week TBA 30% 1,2
    Small Research Project Week TBA 5% 1,2,3
    Final Exam Week TBA 45% 1,2


    Assessment Detail

    1. Homework will be posted each week. The students will be asked to form groups at the beginning of the course and do the  exercises in these groups. 5 of these homeworks will be graded, the rest are not graded.  The homeworks that will be graded will be announced on the course page in advance and will have a clearly marked due date. No individual work will be accepted for this  component. The group homework is submitted online. The tutor will mark one question of his/her choice in each graded homework. At the end of the course, best 3 out of 5 marks will count toward the semester grade for this component.

    2. There will be 2 homework assignments to be submitted individually throughout the course. The dates and submission guidelines will be announced on the course page. At the end of the course, best 1 out of 2 marks will count toward the semester grade for this component.

    3. Small Research Project: There will be a small research project towards the end of the course done in groups, if possible. The due dates will be announced on the course web page in due course. The project will be discussed in the tutorials. Students will be presenting their finding during one of the tutorials as well.

    Unless there are valid reasons and documentation, missed tests, projects or examination will be graded 0. Please refer to the Modified Arrangements for Coursework Assessment Policy http://www.adelaide.edu.au/policies/3303  (and the Schedule to the Policy) for further details about eligibility and application forms. Missed or late submissions of any assignments will not be accepted and will be graded 0.

    Submission
    Submission of the assignments is required as per instructions on MyUni.
    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.

    Submission of the assignments is required as per instructions on MyUni.

  • 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
  • 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’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.