ECON 7243 - Applied Econometrics PG

North Terrace Campus - Semester 1 - 2022

This course covers the estimation, inference and identification of linear regression models. It focuses on applying econometric techniques to real-world problems, and on interpreting 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 7243
    Course Applied Econometrics PG
    Coordinating Unit School of Economics
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Incompatible ECON 7001; ECON 3502; ECON 3530
    Course Description This course covers the estimation, inference and identification of linear regression models. It focuses on applying econometric techniques to real-world problems, and on interpreting 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.
    Course Staff

    Course Coordinator: Dr Nadya Baryshnikova

    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. Explain econometric concepts and results intuitively
    2. Proficiently use STATA for econometric and statistical analysis
    3. Conduct independent data analysis and inquiry using the tools of statistics and econometrics
    4. Derive main econometrics results mathematically
    5. Present and discuss methodology and results in groups
    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.

    1,3,4

    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.

    5

    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.

    2,3,4

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    3
  • Learning Resources
    Required Resources
    Wooldridge J.M. (2016) Introductory Econometrics, 6th Edition. Cengage Learning.
    Recommended Resources
    Angrist J.D. and Pischke J.S. (2008) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
    Online Learning
    MyUni Course WebPage provides lecture notes, computer lecture notes, homework questions, solutions and practice exams. Please check this page frequently for important announcements and corrections.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    2 hours of weekly lectures and weekly one hour tutorials.

    Students who are studying offshore are able to participate in all learning activities through online learning.

    Please consider the use of Zoom or any other preferred software for your online meetings.
    Workload

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

    The standard postgraduate 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 online lectures and one hour of tutorials each week, which means that students should undertake nine hours of self-study each week of the teaching term.
    Learning Activities Summary
    The tentative outline of the course (subject to change) is:

    1. Review of Mathematical Tools, Probability Distributions and Statistical Inference (Wooldridge: Appendices A-C)
    a. Basic mathematical tools
    b. Probability distribution
    c. Point and interval estimation
    d. Large sample properties of estimators
    e. Hypothesis testing and confidence intervalslg
    f. Matrices

    2. Linear Regression Analysis (Wooldridge: Chapters 1-3)
    a. Economic Data
    b. Simple linear regression and ordinary least squares (OLS) estimation
    c. Multiple linear regression
    d. The properties, expected value and the variance of the OLS estimator

    3. Issues in Multiple Regression Analysis (Wooldridge: Chapters 4-7)
    a. Inference and hypothesis testing
    b. Large sample properties of the OLS estimator
    c. Other functional form
    d. Goodness of fit
    e. Qualitative data (Binary variables)

    4. Heteroskedasticity (Wooldridge: Chapter 8)
    a. Heteroskedasticity-robust inference
    b. Testing for heteroskedasticity
    c. Weighted least squares estimation

    5. Specification and Data Issues (Wooldridge: Chapter 9)
    a. Functional form misspecification
    b. Proxy variables
    c. Measurement errors

    Subject to time availability, one or more of the following topics will be covered:

    6. Panel Data (Wooldridge: Chapters 13-14)
    a. Fixed effects estimation
    b. Random effects estimation

    7. Limited Dependent Variable Models and Sample Selection Corrections (Wooldridge: Chapter 17)
    a. Logit and probit models
    b. Tobit models
    c. Poisson regression model
    d. Models with censored and truncated data
    e. Sample selection

    8. Instrumental Variables Estimation and Simultaneous Equations Model (Wooldridge: Chapters 15-16)
    a. Instrumental variables
    b. Two-state least squares estimation
    c. Simultaneity bias in OLS
    Specific Course Requirements
    Homework completion requires access to STATA. You may use the computer labs on campus. Please refer to http://www.adelaide.edu.au/its/student_support/labs/ for further details. If you do not have a copy on your home device, please consult Information Technology's  Student Software guide.

    For course related questions, students are encouraged to utilise the discussion board or the designated office hours of the lecturer and the tutor.
  • 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 Outcomes
    Oral Presentation Group 10% 1-5
    Group Homeworks Group 10% 1-5
    Individual Homeworks Individual 30% 1-4
    Final Exam Individual 50% 1-4
    Assessment Related Requirements
    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.

    Some assignments require to use STATA which is installed in the computer labs or may be accessed on
    your personal devices. Please allow additional time for completing the assignments as the computer labs may not be
    always available
    Assessment Detail
    Further details will be provided on MyUni.
    Submission
    Submission of the assignments is required as per instructions 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 (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.

  • Student Support
  • Policies & Guidelines
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