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|
Delivery & attendance
|Attendance and learning activities:||Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.|
|Prescribed resources:|| |
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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.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Demonstrate advanced knowledge of linear regression, its maintained assumptions and their relevant statistical properties.
- Use simple/multiple regression models to interpret the underlying relationships between the variables and evaluate their statistical significance through hypothesis testing.
- Demonstrate how to determine, vis-a-vis diagnostic statistics, when the maintained assumptions of the linear regression model are violated and critically evaluate how to address the violations so that correct statistical inference can be drawn.
- Demonstrate the ability to solve business problems using econometrics packages.
- Demonstrate the ability to produce a written report that demonstrates higher order understanding of key concepts in applied econometrics.
|Written Report||Use econometrics software to solve prescribed problems and submit professional reports describing your solutions.||20%||Ongoing||1, 2, 3, 4, 5.|
|Computer-Aided Examination (Open)||Comprehensive final examination in Computer Labs. Exam format is a combination of statistical and spreadsheet software applications (e.g., Eviews, Excel, R) and written answers.||50%||Final Examination Period||1, 2, 3, 4, 5.|
|Computer-Aided Examination (Open)||Midterm examination in Computer Labs. Exam format is a combination of statistical and spreadsheet software applications (e.g., Eviews, Excel, R) and written answers. **This exam is held on Saturday of Week 7.||30%||Week 7 (Mid-Semester Examination Period)||1, 2, 3, 4, 5.|
- * 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.
|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.|
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.
Students must check the [email protected] subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
Homework assignments must be submitted at the beginning of lab session as indicated in the subject outline below. Homework submissions by email will not be entertained and it will result in zero marks. 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.
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.
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
As part of the requirements for Business School quality accreditation, the Bond Business School employs 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.
Goodness of fit is examined through Analysis of Variance (ANOVA). The scaling of variables and their significance are discussed in the regression framework. The functional forms such as inverse, log-log and semi-log regressions are discussed. The practical significance of these non-linear-in-variable models is discussed in detail.1, 2, 4.
Hypothesis testing of multiple hypotheses and overall significance of the multiple regression models using the F-test are the focus of this topic. Model specification issues such as (i) omitted variable Bias and (ii) irrelevant variables are explained in the context of regression properties. Polynomial misspecifications are evaluated through the RESET test.1, 2, 3, 4.
Consequences of violating the assumptions of normality, heteroskedasticity, and multicollinearity are examined in detail. The procedures to detect these violations and their remedial measures to address these problems are also introduced.
The qualitative independent variable models are estimated through (i) intercept dummy model, (ii) slope dummy model, (iii) intercept and slope dummy model, and (iv) interaction between dummies. An extension to multiple categories, seasonal effects and regime effects are estimated and tested in this topic.1, 2, 3, 4.
This topic introduces approaches to modelling qualitative dependent variables including the linear probability model, the logistic regression model and the probit regression model.