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.
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Details
Academic unit: Bond Business School Subject code: ECON71-200 Subject title: Econometrics Subject level: Postgraduate Semester/Year: January 2019 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Computer Lab: x12 (Total hours: 24) - Laboratory
- Seminar: x12 (Total hours: 24) - Seminar 1
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
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. -
Resources
Prescribed resources: Books
- Hill, R.H., William E. Griffiths and Guay C. Lim. (2011). Principles of Econometrics. 5th, John Wiley, Inc.
- Hill, R.H., William E. Griffiths and Guay C. Lim. (2011). Using Eviews for Principles of Econometrics. 5th, John Wiley, Inc.
iLearn@Bond & Email: iLearn@Bond 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 www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | ECON71-200 |
| Subject title: | Econometrics |
| Subject level: | Postgraduate |
| Semester/Year: | January 2019 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| 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: | Books
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|---|---|
| iLearn@Bond & Email: | iLearn@Bond 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 www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
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| Restrictions: |
Nil |
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.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed 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 Written Report Use econometrics software to solve prescribed problems and submit professional reports describing your solutions. 20% Ongoing 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.
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Assessment criteria
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.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| 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 |
| Written Report | Use econometrics software to solve prescribed problems and submit professional reports describing your solutions. | 20% | Ongoing | 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.
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 iLearn@Bond 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.
Academic Integrity
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.
Accessibility and Inclusion 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
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.
Subject curriculum
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Correlations and an Introduction to Simple Linear Regression
The relationship between two variables is established through both correlation and simple linear regression model. The use of the least squares approach to estimate the regression parameters is also introduced.
SLOs included
- 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.
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Inference in the Simple Regression Model
The properties of regression estimates are discussed. Hypothesis testing and confidence intervals are used as a tool to test the characteristics of population regression.
SLOs included
- 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 the ability to solve business problems using econometrics packages.
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Simple Regression Model: ANOVA and Functional Forms
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.
SLOs included
- 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 the ability to solve business problems using econometrics packages.
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Multiple Regression Model
An extension from simple regression is introduced through additional classical reversion assumption of multicollinearity. The interpretation of regression coefficients, hypothesis testing of single restriction, confidence intervals and ANOVA are examined in depth.
SLOs included
- 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 the ability to solve business problems using econometrics packages.
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Further Inference in the Multiple Regression Model
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.
SLOs included
- 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.
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Normality, Heteroskedasticity and Multicollinearity
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.
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Autocorrelation, Instrumental Variable Estimates
Consequences of regression assumptions of autocorrelation and non-stochastic regressors are considered in detail. The procedures to detect these violations and their remedial measures to eliminate the problem are also considered.
SLOs included
- 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.
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Dummy Variable Estimates
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.
SLOs included
- 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.
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Limited Dependent Variable Models
This topic introduces approaches to modelling qualitative dependent variables including the linear probability model, the logistic regression model and the probit regression model.
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Generalised Linear Model
This topic includes the concepts of exponential families, Poisson Regression Models, coefficients estimates and confidence intervals, model simplification and deviance and residual diagnostics.
SLOs included
- Demonstrate advanced knowledge of linear regression, its maintained assumptions and their relevant statistical properties.
- 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.