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: May 2017 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Computer Lab: x11 (Total hours: 22) - Laboratory
- Seminar: x12 (Total hours: 24) - Seminar 1
- Tutorial: x11 (Total hours: 22) - Lab Tutorial 2
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. -
Resources
Prescribed resources: Books
- 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.
Others
- Gujarati, D. (2003). Basic Econometrics. McGraw-Hill, 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: | May 2017 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| 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: | Books
Others
|
|---|---|
| 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. No Prior Knowledge Required |
| 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:
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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 Paper-based Examination (Open) Final Examination - in Computer Labs 50% Final Examination Period 1,2,3,4 Paper-based Examination (Open) Mid-semester Examination - in Computer Labs 30% Final Examination Period 1,2,3,4 Written Report Homeworks 20% Ongoing 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.
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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 |
|---|---|---|---|---|
| Paper-based Examination (Open) | Final Examination - in Computer Labs | 50% | Final Examination Period | 1,2,3,4 |
| Paper-based Examination (Open) | Mid-semester Examination - in Computer Labs | 30% | Final Examination Period | 1,2,3,4 |
| Written Report | Homeworks | 20% | Ongoing | 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 iLearn@Bond 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.
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
Subject curriculum
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Week 1: Lecture 1: Review of Basic Statistics - NO LABS
Measures of Central Tendancy, Varaibility and Shape, Normal Distribution and Correlations.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 2: Lecture 2: Simple Linear Regression Model
Regression Assumptions, Estimation and Properties of Regression Estimates.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 3: Lecture 3: Inference in the Simple Regression Model
t-distribution and Hypothesis Testing on Regression Parameters and Confidence Intervals.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 4: Lecture 4: Simple Regression Model: ANOVA and Functional Forms
ANOVA, Goodness of Fit, Scalling and Functional Forms. HW1 due during computer lab.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 5: Lecture 5: Multiple Regression Model
Assumptions, Estimation, Hypothesis Testing (t-distribution) and Confidence Intervals.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 6: Lecture 6: Further Inference in the Multiple Regression Model
Testing Multiple Hypothesis (F-test), Model Specification and RESET Test. HW2 due during computer lab.
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Week 7: No Lectures / no lab sessions - Midterm Exam (Saturday)
Midterm Exam on Saturday.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 8: Lecture 7: Normality, Heteroskedasticity and Multicollinearity
(1) Normality, (2) Consequences of Heteroskedsticity, Detection of Heteroskedasticity and Corrections for Heteroskedasticity and (3) Multicollinearity.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 9: Lecture 8: Autocorrelation, Instrumental Variable Estimates
(1) Consequence of Autocorrelations, Detection of Autocorrelations and Corrections for Autocorrelations, (2) Consequence of Non-stochastic Regressors and IV Estimates.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 10: Lecture 9: Dummy Variable Estimates
Modelling Quality Choice Independent Variables with Intercept and Slope Dummies. HW3 due during computer lab.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 11: Lecture 10: Limited Dependent Variable Models
Linear Probability Model, Logistic Regression Model and Probit Model.
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 12: Revision during Lecture. No Lab session
HW4 due (during computer labs time)
SLOs included
- Understand the Classical Linear Regression Model, its maintained assumptions and relevant properties. This model is by far the most frequently used by researcher 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.
- Apply the Classical Regression Models to decision making problems for the purpose of hypothesis testing and prediction.
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.