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ECON12-200: Linear Models and Applied Econometrics May 2017 [Standard]

General information

Econometrics is a sub-discipline of both statistics and economics and presents one interface between statistical theory and the real world. It provides the tools with which to test hypotheses and to generate forecasts of business activity. Topics include the classical regression model, remedial measures for violation of regression assumptions, binary choice models, panel data models and their applications. The technique such as hypothesis testing and its application will allow students to specialise in areas such as market research and other disciplines. The skills that students will develop in this subject are crucial in any applied work and will constitute an essential ingredient in most jobs in the field of business application, whether in the public or private sector.


Academic unit:Bond Business School
Subject code:ECON12-200
Subject title:Econometrics
Subject level:Undergraduate
Semester/Year:May 2017
Credit points:10

Delivery & attendance

Delivery mode:


Workload items:
  • Tutorial: x11 (Total hours: 22) - Lab Tutorial 2
  • Computer Lab: x11 (Total hours: 22) - Laboratory
  • Seminar: x12 (Total hours: 24) - Seminar 1
Attendance and learning activities: Attendance Attendance is compulsory. Materials that are discussed in both lecture and lab sessions are EXAMINABLE. You run the risk of missing important material as well as crucial guidelines to work through assignment problems and exams if you do not attend. Use of iLearn: Lecture notes, lab session questions, assignment / homework questions are available on iLearn. All materials are available in PDF form. The lecture and labs will be streamed using tablets and will be available in iLearn. The original powerpoint slides will not be posted in editable form due to copy right regulations. Answers to the lab sessions will be available after completing the relevant sessions. Answers to the homework questions will be available after the due date. Students should visit the announcement section in iLearn on a regular basis as new information (eg, any change in class schedule, mid-term and final exam venue, etc.) will be posted from time to time. Lecture and Lab Streaming: Lecture / Lab Streaming and in-class handwritten notes will be available in Learn.


Prescribed resources:
  • Hill, R.H., William E. Griffiths and Guay C. Lim. (2011). Principles of Econometrics. 4th, John Wiley, Inc.
  • Hill, R.H., William E. Griffiths and Guay C. Lim. (2011). Using Eviews for Principles of Econometrics. 4th, John Wiley, Inc.:
  • Gujarati, D. (2003). Basic Econometrics. [Book] McGraw-Hill, Inc..
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
[email protected] & Email:[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

To access these services, log on to the Student Portal from the Bond University website as

Enrolment requirements

Requisites: ?


Restrictions: ?


Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researchers in many disciplines such as Accounting, Economics, Finance, Marketing and Management and among others to analyse the behaviour of the variables of interest in their relevant disciplines.
  2. Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
  3. Learn how to determine, vis-a-vis diagnostic statistics, when the maintained assumptions of the Classical Linear Regression Model are violated and how to address the violations so that correct statistical inference can be drawn.
  4. Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.


Assessment details

TypeTask%Timing*Outcomes assessed
Essay Homeworks 20% Ongoing 1, 2, 3, 4.
Paper-based Examination (Open) Mid-semester Examination - in Computer Labs 30% Mid-Semester Examination Period 1, 2, 3, 4.
Paper-based Examination (Open) Final Examination - in Computer Labs 50% Final Examination Period 1, 2, 3, 4.
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

Assessment criteria

High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

Quality assurance

For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Study information

Submission procedures

Students must check the [email protected] subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

Homework assignment questions will be assigned for each topic.There will be 4 homeworks for submission. Homework assignments must be submitted at the beginning of lab session as indicated in the subject outline below. The best three will count towards your homeworks grade. Homework submissions by email will not be entertained and it will result in zero marks. Students may work on their assignment in a group but should write their assignment independently in their own words. If it is not written independently, it will be considered as plagiarism. Due to the voluntary nature of best three assignments out of total of four homework assignments, late submission will result in zero marks.

Policy on plagiarism

University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Disability support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

Additional subject information

Midterm Exam: Week 7 - Saturday

Subject curriculum

Measures of Central Tendancy, Varaibility and Shape, Normal Distribution and Correlations

Regression Assumptions, Estimation and Properties of Regression Estimates

1, 4.

t-distribution and Hypothesis Testing on Regression Parameters and Confidence Intervals.

1, 2, 4.

ANOVA, Goodness of Fit, Scalling and Functional Forms. HW1 due during computer lab

1, 2, 4.

Assumptions, Estimation, Hypothesis Testing (t-distribution) and Confidence Intervals

1, 2, 4.

Testing Multiple Hypothesis (F-test), Model Specification and RESET Test. HW2 due during computer lab

(1) Normality, (2) Consequences of Heteroskedsticity, Detection of Heteroskedasticity and Corrections for Heteroskedasticity and (3) Multicollinearity

1, 2, 3, 4.

(1) Consequence of Autocorrelations, Detection of Autocorrelations and Corrections for Autocorrelations, (2) Consequence of Non-stochastic Regressors and IV Estimates

1, 2, 3, 4.

Modelling Quality Choice Independent Variables with Intercept and Slope Dummies. HW3 due during computer lab

1, 2, 4.

Linear Probability Model, Logistic Regression Model and Probit Model

1, 2, 3, 4.

HW4 due (during computer labs time)

1, 2, 3, 4.
Approved on: Mar 21, 2017. Edition: 1.1