最新糖心Vlog

ECON 2515 - Intermediate Applied Econometrics II

North Terrace Campus - Summer - 2022

This course provides an introduction to the econometric techniques used to analyse data sets in economics, business and finance. It builds on basic statistics, inference and regression as covered in introductory statistics courses but does not include time series econometrics. The focus is on understanding the methods involved, using statistical software to provide the results and then interpreting and commenting on these results. The course reviews basic statistics, regression and inference, and then introduces multiple regression analysis, which remains the most commonly used statistical technique in econometrics. The remainder of the course considers various practical aspects of linear regression models and may include dummy variables, different functional forms and the consequences of violation of the classical regression assumptions.

  • General Course Information
    Course Details
    Course Code ECON 2515
    Course Intermediate Applied Econometrics II
    Coordinating Unit Economics
    Term Summer
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week. Intensive in Summer Semester
    Available for Study Abroad and Exchange Y
    Prerequisites ECON 1008 or ECON 1011 or equivalent
    Incompatible ECON 2504
    Assumed Knowledge ECON 1005 and ECON 1012
    Restrictions Not suitable for BEc(Adv) students
    Assessment Typically assignments, mid-term test and final exam
    Course Staff

    Course Coordinator: Patricia Sourdin

    Course Timetable

    The full timetable of all activities for this course can be accessed from .

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will be able to:

    1. Have an in-depth knowledge of Economic data structure and use adequate visual tools to present data
    2. Estimate simple and multiple linear regressions with quantitative data
    3. Test and correct for heteroscedasticity
    4. Estimate linear regressions with qualitative data
    5. Interpret outcomes of the regressions
    6. Discuss and communicate methodology and results in a team
    最新糖心Vlog Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    最新糖心Vlog Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    1-5

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    3,5

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    6

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

    5
  • Learning Resources
    Required Resources
    The required textbook for this course is Introductory Econometrics by Jeffrey M. Wooldridge, Mokhtarul Wadud, Jenny Lye, 2nd edition
    Online Learning
    MyUni Course WebPage provides lecture notes, computer lecture notes, homework questions and solutions. Please check this page frequently for important announcements and corrections.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Summer School is intensive over 6 weeks. There are 24 hours of lectures and 11 hours of tutorials.
    Students are expected to attend tutorials and they will not be recorded. If any tutorial is missed, students will need to catch up on any missed material.
    Workload

    No information currently available.

    Learning Activities Summary
    Topics Title Chapters
    1 Introduction and review Chapters 1 and 3
    2 The simple regression model Chapter 4
    3 Multiple regression Analysis: estimation Chapter 5
    4 Multiple regression analysis: inference Chapter 6
    5 Model specification Chapter 7
    6 Multiple regression analysis with qualitative information: binary (or dummy) variables Chapter 8
    7 Heteroscedasticity Chapter 9
  • Assessment

    The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Weighting Learning Outcome
    Assignments Group 30% 1-6
    Mid-term test Individual 20% 1-5
    Final Exam Individual 50% 1-5
    Total: 100%
    Assessment Related Requirements

    Some assignments and homework require you to use STATA software. The 最新糖心Vlog has recently purchased a site licence for Stata, and Stata 17.0 MP is now available in the Software Centre to download.  Any student who needs it has access to it as long as they have a compatible operating system and memory etc.  
    Students may download it to one personally-owned device.   See the ITDS ‘Software for Students’ page: /technology/your-services/software/software-for-students.  The info for Stata is down at the bottom of the list.

    Stata is also installed in the computer labs or may be accessed via ADAPT on your personal devices.
    Please allow additional time for completing the assignments as the computer labs may not always be available.

    Assessment Detail


    1.  There will be 1 assignment to be submitted in groups throughout the course. No individual work will be accepted for this component. Further details will be posted to MyUni.  Modified arrangements due to exceptional circumstances will be considered on a case-by-case basis. The dates and submission guidelines will be announced on MyUni. The assignment is worth 30% of the final grade and is not redeemable.

    2. There will be a mid-semester test worth 20% of the final grade. Further details will be announced on MyUni. The test is redeemable if students choose not, or are unable, to do the tests. The weighting of the missed test will be added to the weighting of the final exam.
    Submission
    Submission of the assignments is required as per instructions on MyUni. Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process, and may affect marks. Marks cannot be awarded for answers that cannot be read or understood.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through .

  • Student Feedback

    The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

    The revisions to this course, based on student feedback, include a clearer structure of topics, clearer due dates, and making the midterm redeemable.
  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.