ECON 3502 - Econometrics III

North Terrace Campus - Semester 1 - 2016

The course focuses on the estimation, inference and identification of linear regression models. Particular attention is paid to the econometric theory, to the application of econometrics to real-world problems, and to the interpretation of the estimation results. The first part of the course includes a review on statistics and an introduction to large sample theory. The second part of the course is focuses on issues in linear regressions including model misspecification, measurement errors, and endogenous regressors. Topics on instrumental variable regressions and panel data will be covered. the course will include the use of STATA, a standard software for econometric and statistical analysis.

  • General Course Information
    Course Details
    Course Code ECON 3502
    Course Econometrics III
    Coordinating Unit Economics
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites ECON 2504
    Incompatible ECON 3013. Not permitted to be undertaken after ECON 3507 Econometric Theory III
    Assessment Typically mid-semester exam & final exam
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    Dr Nadya Baryshnikova
    Office location: Nexus 10, Level 4, Room 4.04
    Telephone: 8313 4821
    Office hours: To be advised on myUni
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

    The course aims to achieve the following outcomes. It will provide students:

    1. with knowledge on the fundamentals of econometrics and its application

    2. with knowledge and proficiency on the use of statistical packages for econometric and statistical analysis

    3. with the ability to conduct independent data analysis and inquiry using the tools of statistics and econometrics

    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)
    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
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
  • Learning Resources
    Required Resources
    The required textbook is:
    J.M. Wooldridge, Introductory Econometrics, 5th Edition, South-Western 2012
    Online Learning
    MyUni Course WebPage provides lecture notes, computer lecture notes, homework questions and solutions. Please check this page frequently for important announcements and corrections.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Classes will meet twice per week, for a 2-hour lecture and a 1- hour tutorial.  Students are expected to be present for all lectures and actively participate in all tutorial activities. The lecturer will hold office hours, except for breaks and holidays, with additional hours held by the tutor.

    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 two hours of lectures and one hour of tutorials each week, which means that students should undertake nine hours of self-study each week of the teaching term.

    Weekly group assignments are issued each week. In addition, two homeworks will need to be submitted individually. All students may be asked to present their solutions during each tutorial session.

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

    Learning Activities Summary
    The tentative outline of the course (subject to change) is:

    1. Review of Mathematical Tools, Probability Distributions and Statistical Inference (Wooldridge: Appendices A-C)
    a. Basic mathematical tools
    b. Probability distribution
    c. Point and interval estimation
    d. Large sample properties of estimators
    e. Hypothesis testing and confidence intervals

    2. Linear Regression Analysis (Wooldridge: Chapters 1-3)
    a. Economic Data
    b. Simple linear regression and ordinary least squares (OLS) estimation
    c. Multiple linear regression
    d. The properties, expected value and the variance of the OLS estimator

    3. Issues in Multiple Regression Analysis (Wooldridge: Chapters 4-6)
    a. Inference and hypothesis testing
    b. Large sample properties of the OLS estimator
    c. Other functional form
    d. Goodness of fit

    4. Heteroskedasticity (Wooldridge: Chapter 8)
    a. Heteroskedasticity-robust inference
    b. Testing for heteroskedasticity
    c. Weighted least squares estimation

    5. Specification and Data Issues (Wooldridge: Chapter 9)
    a. Functional form misspecification
    b. Proxy variables
    c. Measurement errors

    Subject to time availability, one or more of the following topics will be covered:

    6. Panel Data (Wooldridge: Chapters 13-14)
    a. Fixed effects estimation
    b. Random effects estimation

    7. Limited Dependent Variable Models and Sample Selection Corrections (Wooldridge: Chapter 7)
    a. Logit and probit models
    b. Tobit models
    c. Poisson regression model
    d. Models with censored and truncated data
    e. Sample selection

    8. Instrumental Variables Estimation and Simultaneous Equations Model (Wooldridge: Chapters 15-16)
    a. Instrumental variables
    b. Two-state least squares estimation
    c. Simultaneity bias in OLS
    d. Identifying and estimation a structural equation
    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 for further details.

    Students may discuss homework assignments among themselves. Homework assignments may be submitted individually or as a group of two students belonging to the same tutorial session. If an assignment is turned in as a group, students in the same group will receive the same score.

    For course related questions, students are encouraged to utilise the designated office hours of the lecturer and the tutor. Questions over the telephone are strongly discouraged. Students may utilise the online forum of MyUni.

  • 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

    Students will be assessed based on the following criteria:

    Group Assignments 20%
    Individual Homework Assignments 30%
    Final Exam 50%

    Assessment Related Requirements
    Attendance in class and tutorials is expected.

    Some assignments require to use STATA which is installed in the computer labs or may be accessed via ADAPT on your personal devices. Please allow additional time for completing the assignments as the computer labs may not be always available.
    Assessment Detail

    1. There will be about 7 group assignments in total. The students will be asked to form groups at the beginning of the course and do the  exercises in these groups. No individual work will be accepted for this  component. The group homework will be collected in tutorials. The tutor will mark one question of his/her choice. At the end of the course, best 5 out of 7 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 MyUni. At the end of the course, best 1 out of 2 marks will count toward the semester grade for this component.

    3. The final test will be in the lab during week 13 or 14. The dates will be announced in advance on myUni.

    Homework will be posted almost each week. Missed or late submissions will not be accepted and will be graded 0. Supplementary test or examination will not be given to replace the missed ones. Unless there are valid reasons and documentations, missed test or examination will be graded 0. Please refer to the Modified Arrangements for Coursework Assessment Policy (and the Schedule to the Policy) for further details about eligibility and application forms.

    Each assessment addresses University Graduate Attributes of deep discipline knowledge, critical thinking and problem solving skills, career and leadership readiness to achieve the Course Learning Outcomes 1, 2, and 3. In addition, group homework addressess University Graduate Attributes of teamwork and communication skills and self-awareness and emotional intelligence to achieve the Course Learning Outcomes 2 and 3.

    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.

    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 ( 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.

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