ECON 4018 - Econometrics for Business Research IV(H)

North Terrace Campus - Semester 1 - 2022

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 focuses on issues in linear regressions including model misspecification, measurement errors, and endogenous regressors. Topics typically include instrumental variable regressions and panel data. The course will include the use of STATA, a standard software for econometric and statistical analysis.

  • General Course Information
    Course Details
    Course Code ECON 4018
    Course Econometrics for Business Research IV(H)
    Coordinating Unit Economics
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week, some weeks up to 5 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites ECON 2504 or ECON 1011
    Incompatible ECON 3502
    Restrictions Available only to students enrolled in the Bachelor of Commerce (Honours) or Bahcelor of Finance (Honours)
    Assessment Typically, group and individual assignments, project and final exam.
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    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. Explain econometric concepts and results intuitively
    2. Proficiently use STATA for econometric and statistical analysis
    3. Conduct independent data analysis and inquiry using the tools of statistics and econometrics
    4. Analyse and assess solutions using business data
    5. Present and discuss methodology and results in groups
    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.


    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.


    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.


    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.


    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.

  • Learning Resources
    Required Resources
    The required textbook is:
    J.M. Wooldridge, Introductory Econometrics, 5th Edition, South-Western 2012

    MyUni Course WebPage provides lecture notes, computer lecture notes, homework questions and solutions. Please check this page frequently for important announcements and corrections.
    Recommended Resources
    Angrist J.D. and Pischke J.S. (2008) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Online 2 hours of weekly lectures (may be split into smaller units) and weekly face-to-face one hour tutorials.

    Students who are studying offshore are able to participate in all learning activities through online learning.

    Please consider the use of Zoom or any other preferred software for your group meetings.

    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 online 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. Over the course of the semester there will be an additional six hours of contact in the form of a workshop.
    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.

    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. Please use the discussion board on myUni instead of the email.
  • 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 Task Type Weighting Learning Outcome
    Group Assignments Group 20% 1-3, 5
    Individual Assignments Individual 30% 1
    Project Individual 20% 1-4
    Final Examination Individual 30% 1-3
    Assessment Related Requirements
    Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process, and may affect marks. Marks cannot be awarded for answers that cannot be read or understood.

    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 always be available
    Assessment Detail
    1. There will be 3 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 tutor will mark one question of his/her choice from each assignment. At the end of the course, best 2 out of 3 marks will count toward the semester grade for this component. Because not all of these marks count for assessment, no special consideration will be given to students who do not submit the homework (or submit it late) for medical, compassionate or any other reason.

    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. Because the best mark out of two counts for assessment, no special consideration will be given to students who do not submit the homework (or submit it late) for medical, compassionate or any other reason. 

    3. The project will be focused on business data using the models that are taught in class.

    4. The final test may be in the lab during week 13 or 14. The dates will be announced in advance on MyUni. All requirements will be posted on MyUni.

    There is no extra work that can be done to redeem individual components of assessment during the semester, no matter the reason;
    Assessment marks prior to the final exam may be displayed on the course website. Students are encouraged to check their marks and notify the course coordinator of any discrepancies.

    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.
    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:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported 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 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.

  • 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.