ECON 3502 - Econometrics III

North Terrace Campus - Semester 2 - 2022

The course focuses on the estimation, inference and identification of linear regression models. Particular attention is paid to the econometric theory, to the application of econometrics to real-world problems, and to the interpretation of the estimation results. The first part of the course includes a review on statistics and an introduction to large sample theory. The second part of the course focuses on issues in linear regressions including model misspecification, measurement errors, and endogenous regressors. Topics typically include instrumental variable regressions and panel data. The course will include the use of STATA, a standard software for econometric and statistical analysis.

  • General Course Information
    Course Details
    Course Code ECON 3502
    Course Econometrics III
    Coordinating Unit Economics
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites ECON 2504 or ECON 2515 or ECON 2517
    Assumed Knowledge ECON 1005 or equivalent
    Restrictions Only available to B.Economics (Advanced) students
    Assessment Typically group and individual assignments and final exam
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    Dr Nadezhda Baryshnikova
    Office location: Nexus 10, Level 4, Room 4.04
    Telephone: 8313 4821
    Office hours: To be advised on myUni
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

    On successful completion of this course, students will be able to:

    1. demonstrate an in-depth knowledge of regression analysis

    2. explain advanced econometric concepts including panel data methods, IV regressions, limited dependent variable models and advanced topics in times series

    3. proficiently use statistical software (usually STATA, Matlab, or R) for econometric and statistical analysis

    4. conduct independent data analysis and inquiry using advanced econometric methods

    5. interpret results and shortcomings of the analysis.

    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.


    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.


    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.


    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.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    The required textbook is:
    J.M. Wooldridge, Introductory Econometrics, 7th Edition, South-Western 2019
    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
    2 hours of weekly lectures and 2 hours of weekly lab workshops

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

    The standard undergraduate workload for a full-time student is 48 hours per week which equates to 12 hours per 3 unit course. This course has two hours of lectures and two hours of workshops each week, which means that students should undertakeat least 8 hours of self-study each week of the teaching term.
    Learning Activities Summary
    Tentative schedule (subject to change)


    1 Module 1 – Review of Multiple Linear Regression
    1.1 Definition of the multiple linear regression model
    1.2 Classical assumptions
    1.3 Matrix notations and derivations of the ordinary least squares (OLS) estimates
    1.4 Finite Samples Properties of OLS: Unbiasedness, Gauss-Markov Theorem (Section E.2, Wooldridge textbook)
    1.5 Asymptotic Analysis: Consistency and Asymptotic Normality (Section E.4, Wooldridge textbook)

    2-3 Module 2 – Pooling Cross Sections across Time: Simple Panel Data Methods (Chapter 13, Wooldridge textbook)
    2.1 Pooling independent cross sections across time
    2.2 Policy analysis with pooled cross sections
    2.3 Two-period panel data analysis
    2.4 Policy analysis with two-period panel data
    2.5 Differencing with more than two time periods

    4-5 Module 3 – Advanced Panel Data Methods (Chapter 14, Wooldridge textbook)
    3.1 Fixed effects estimation
    3.2 Random effects models
    3.3 The correlated random effects approach

    6-8 Module 4 – Instrumental Variables Estimation and Two Stage Least Squares (Chapter 15, Wooldridge textbook)
    4.1 Motivation: omitted variables in a simple regression model
    4.2 IV estimation of multiple regression model
    4.3 Two stage least squares
    4.4 IV solutions to errors-in-variables problems
    4.5 Testing for endogeneity and test overidentifying restrictions

    9-10 Module 5 – Limited Dependent Variable Models (Chapter 17, Wooldridge textbook)
    5.1 Logit and Probit models for binary response
    5.2 Tobit model for corner solution responses
    5.3 Censored and truncated regression models
    5.4 Sample selection corrections

    11-12 Module 6 – Advanced Time Series Topics (Chapter 18, Wooldridge textbook)
    6.1 Infinite distributed lag models
    6.2 Testing for unit roots
    6.3 Spurious regression
    6.4 Forecasting

    Specific Course Requirements
    Homework completion will require access to STATA. STATA may be accessed via IT software webpage or in the computer lab in Nexus 10. Please refer to for further details.

    For course related questions, students are encouraged to utilise the discussion board or the designated office hours of the lecturer.
  • 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 Word Count/ Time Due Learning Outcome
    Projects Group, Summative 30% Varying TBA 1-5
    Weekly Activities* Individual, Summative 20% Varying Weekly 1-5
    Final Exam Individual, Summative 50% 3 hours Exam Period 1-5

    * Weekly Activities consist of quizzes and varying participation activities.

    There are NO hurdle requirements.
    Assessment Related Requirements
    Some assignments require STATA which is installed in the computer labs or may be installed on your personal device. Please allow additional time for completing the assignments as the computer labs may not be always available.

    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
    Assessment Detail
    Further details will be provided on MyUni.
    Details will be provided on myUni.
    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 Access Adelaide.

  • 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 ( 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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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