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: ECON12-200 Subject title: Econometrics Subject level: Undergraduate Semester/Year: January 2018 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Seminar: x12 (Total hours: 24) - Seminar 1
- Computer Lab: x11 (Total hours: 22) - Laboratory
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
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.---------------------------------------------------------------------------------------------------------- Mid-semester Exam: The mid-term exam will be held in week 7 and will constitute 30% towards your final grade. It will cover material up to and including week 5.------------------------------------------------------------------------------------------------------ Final Exam: The final exam will cover material from the whole subject. The exam will have more weight from post-mid-term labs and lectures, but will include questions from pre-mid-term topics as well. The final exam will be scheduled and held during the Examination Week.------------------------------------------------------------------------------------------ Both mid-term and final exams will be OPEN BOOK exams. Printed materials (only) such as text book, lecture notes, labs and homework answers, etc. are allowed for the OPEN BOOK exams. Any form of electronic devices are prohibited for the Exam.-------------------------------------------------------------------------------------------------------------------- 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.
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 |
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Subject code: | ECON12-200 |
Subject title: | Econometrics |
Subject level: | Undergraduate |
Semester/Year: | January 2018 |
Credit points: | 10.000 |
Timetable: | https://bond.edu.au/timetable |
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Delivery mode: | Standard |
Workload items: |
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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.---------------------------------------------------------------------------------------------------------- Mid-semester Exam: The mid-term exam will be held in week 7 and will constitute 30% towards your final grade. It will cover material up to and including week 5.------------------------------------------------------------------------------------------------------ Final Exam: The final exam will cover material from the whole subject. The exam will have more weight from post-mid-term labs and lectures, but will include questions from pre-mid-term topics as well. The final exam will be scheduled and held during the Examination Week.------------------------------------------------------------------------------------------ Both mid-term and final exams will be OPEN BOOK exams. Printed materials (only) such as text book, lecture notes, labs and homework answers, etc. are allowed for the OPEN BOOK exams. Any form of electronic devices are prohibited for the Exam.-------------------------------------------------------------------------------------------------------------------- Lecture and Lab Streaming: Lecture / Lab Streaming and in-class handwritten notes will be available in Learn. |
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: |
Pre-requisites:Co-requisites:There are no co-requisites |
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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:
- 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.
- 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 Computer-Aided Examination (Open) Final Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews and Excel 50% Final Examination Period 1,2,3,4 Computer-Aided Examination (Open) Mid-semester Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews and Excel. 30% Mid-Semester Examination Period 1,2,3,4 Written Report Individual Assignment (due in week 4 , week 6 and week 9) 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 |
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Computer-Aided Examination (Open) | Final Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews and Excel | 50% | Final Examination Period | 1,2,3,4 |
Computer-Aided Examination (Open) | Mid-semester Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews and Excel. | 30% | Mid-Semester Examination Period | 1,2,3,4 |
Written Report | Individual Assignment (due in week 4 , week 6 and week 9) | 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. |
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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 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
Midterm Exam: Week 7 - Saturday
Subject curriculum
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Week 1: Lecture 1: Correlations and An Introduction to Simple Linear Regression Model
Correlation Coefficient, Regression Assumptions and Estimation.
SLOs included
- 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.
- Use econometrics package EVIEWS most frequently used by practitioners to analyse the data.
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Week 2: Lecture 2: Inference in the Simple Regression Model. Lab 1
Properties of Regression Estimates, t-distribution and Hypothesis Testing on Regression Parameters and Confidence Intervals.
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Week 3: Lecture 3: Simple Regression Model: ANOVA and Functional Forms. Lab 2
ANOVA, Goodness of Fit, Scalling and Functional Forms.
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Week 4: Lecture 4: Multiple Regression Model. Lab 3
Assumptions, Estimation, Hypothesis Testing (t-distribution) and Confidence Intervals--Assignment1 due during computer lab session.
SLOs included
- 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.
- 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: Further Inference in the Multiple Regression Model. Lab 4
Testing Multiple Hypothesis (F-test), Model Specification and RESET Test.
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Week 6: Lecture 6: Normality, Heteroskedasticity and Multicollinearity. Lab 5
(1) Normality, (2) Consequences of Heteroskedsticity, Detection of Heteroskedasticity and Corrections for Heteroskedasticity and (3) Multicollinearity - - Assignment2 due during computer lab session.
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Week 7: Mid Semester Exam(Saturday) Lecture 7 Autocorrelation, Instrumental Variable Estimate, Lab 6
Mid Semester Exam(Saturday) Lecture 7: (1) Consequence of Autocorrelations, Detection of Autocorrelations and Corrections for Autocorrelations, (2) Consequence of Non-stochastic Regressors and IV Estimates. Lab 6.
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Week 8: Lecture 8: Dummy Variable Estimates. Lab 7
Modelling Quality Choice Independent Variables with Intercept and Slope Dummies, Chow Test.
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Week 9: Lecture 9: Limited Dependent Variable Models. Lab 8
Linear Probability Model, Logistic Regression Model and Probit Model - Assignment 3 due during computer lab session.
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Week 10: Lecture 10: Pooled Regression Model, Fixed and Random Effect Models. Lab 9
Pooled Regression Model, Fixed and Random Effect Models.
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Week 11: Lab 10
No new Lectures, Lab 10 only.
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Week 12: No new Lectures No Lab, revision only
No Lectures, No Labs, revision only.