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Course Outlines

ECON 2515 - Intermediate Applied Econometrics II

North Terrace Campus - Semester 2 - 2025

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.

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  • General Course Information
    Course Details
    Course Code ECON 2515
    Course Intermediate Applied Econometrics II
    Coordinating Unit Economics
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Incompatible ECON 2517
    Assumed Knowledge ECON 1012, ECON 1005 or ECON 1010, ECON 1008 or ECON 1011
    Restrictions Not suitable for students enrolled in B.Eco(Adv) program
    Assessment Typically assignments, mid-term test and final exam
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    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
    University Graduate Attributes

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

    University 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
    Two hours of weekly face to face workshops and weekly face-to-face one hour tutorials starting in week 2.



    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.


    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The University expects full-time students (i.e. those taking 12 units per semester) to devote a total of 48 hours per week to their studies. This translates to 12 hours per week for a semester course.

    Learning Activities Summary
    Modules
    Module 1 - Nature of Econometrics and Economic Data
    1.1- What is econometrics?
    1.2- Steps in empirical economic analysis
    1.3- The structure of economic data
    1.4- Graphing data
    1.5- Causality and the notion of Ceteris Paribus in econometric analysis
    Module 2- The Simple Linear Regression Model
    2.1- Definition of the simple linear regression model
    2.2- Deriving the ordinary least square estimates
    2.3- Examples of simple regression obtained using real data
    2.4- Properties of OLS
    2.5- Unit of measurement and functional form
    2.6- Unbiasedness, consistency and variances of the OLS estimates
    Module 3- Mulitple Linear Regression Model: Estimation
    3.1- Motivation
    3.2- Mechanism and interpretation of ordinary least square equation (OLS)
    3.3- Properties of OLS estimators
    Module 4- Multiple Linear Regression Model: Inference
    4.1- Sample distribution of the OLS estimators
    4.2- Testing hypotheses about a single population parameter: The t-test
    4.3- Confidence intervals
    4.4- Testing hypotheses about a single linear combination of the parameter
    4.5- Testing multiple linear restrictions: The F-test
    4.6- Confidence intervals for predictions
    4.7- Reporting regression results
    Module 5- Heteroscedasticity
    5.1- Definition of heteroscedasticity
    5.2- Testing for heteroscedasticity
    5.3- Correcting heteroscedasticity
    Module 6-Multiple regression analysis with qualitative information: binary (or dummy) variables
    6.1- Describing qualitative information
    6.2- A single dummy independent variable
    6.3- Using dummy variables for multiple categories
    6.4- Interactions involving dummy variables
    6.5- A binary dependent variable: The linear probability model (LPM)





    Specific Course Requirements
    Assignment completion will require access to computer software STATA.
    The University has made Stata 17 software available for students to download to a personal Mac/PC. Please follow the instructions at the
    link Stata software

    Alternatively, you may use the computer labs on campus. Please refer to this for further details.

    For course related questions, students are encouraged to use the online forum of MyUni, email the lecturer, or ask questions in lectures or tutorials.
  • Assessment

    The University'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
    Assignment Group 30% 1-6
    Weekly activities Individual 20% 1-5
    Final Exam Individual 50% 1-5
    Total: 100%
    Assessment Related Requirements
    Some assignments require you to use STATA which is installed in the computer labs or may be installed on your personal Mac/PC through ITS.
    Assessment Detail
    Weekly activities consist of quizzes and participation.

    Further details will be provided on MyUni and in the first week of lectures.

    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 University 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 University 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

    This section contains links to relevant assessment-related policies and guidelines - all university policies.

  • 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 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
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