ECON 2517 - Intermediate Econometrics II

North Terrace Campus - Semester 2 - 2022

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

Open All

Course Details

Course Code	ECON 2517
Course	Intermediate Econometrics II
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
Incompatible	ECON 2504
Restrictions	Only available to B.Economics (Advanced) students
Assessment	Typically assignments, mid-term test and final exam

Course Staff

Course Coordinator: Dr Qazi Haque

Qazi Haque
Location: Room 4.47, Nexus 10 Tower
Telephone: 8313 4927
Email: qazi.haque@adelaide.edu.au

Course Timetable

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

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. Writing regressions in matrix form
3. Estimating simple and multiple linear regressions with quantitative data
4. Testing and correcting for heteroscedasticity and autocorrelation
5. Estimating linear regressions with qualitative data
6. Interpreting and assessing outcomes of the regressions

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.	1-6
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.	3,6
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.	6

Learning & Teaching Modes

Attend face-to-face weekly lectures (2 hours) and workshops (2 hours).

This subject is only available for face-to-face (on-campus) students.

Workload

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

The 最新糖心Vlog 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

Tentative Schedule (subject to change):
Week/s	Modules
1	Module 1- Nature of Econometrics and Economic Data
2-5	Module 2 - Linear Regression Models 2.1- Definition of the simple and multiple linear regression model 2.2- Classical assumptions 2.3- Matrix notation and derivation the ordinary least squares (OLS) estimates 2.4- Properties of OLS: Unbiasedness, consistency and variances of the OLS estimates 2-5- Testing hypotheses about regression population parameters 2.5.1. Testing hypotheses about a single population parameter: The t-test 2.5.2. Confidence intervals 2.5.3. Testing multiple linear restrictions: The F-test
6	Module 3- Model Specification 3.1- Functional form 3.2- Specification error 3.3- Multicollinearity
7	MidTerm Test
8-9	Module 4- Multiple Regression Analysis with qualitative information 4.1- Describing qualitative information 4.2- Using dummy independent variables 4.3- A binary dependent variable: The linear probability model (LPM)
10-12	Module 5- Heteroscedasticity and Autocorrelation 5.1- Definition of heteroscedasticity 5.2- Testing for heteroscedasticity 5.2- Correcting heteroscedasticity 5.3- Definition of autocorrelation 5.4- Testing for autocorrelation 5.5- Correcting autocorrelation

Specific Course Requirements

Assignment completion may require access to computer software STATA. If you do not have STATA at home, you may use the computer labs on campus. Please refer to http://www.adelaide.edu.au/its/student_support/labs/ for further details.

For course related questions, students are encouraged to utilise the designated office hours of the lecturer and the tutors. Questions over the telephone are strongly discouraged. Students are encouraged to utilise the online forum on MyUni instead of emailing questions.

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

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

Assessment Summary

Assessment Task	Task Type	Weighting	Learning Outcome
Group Assignments	Group	30%	1-6
Midterm exam	Individual	20%	1-6
Final examination	Individual	50%	1-6
Total		100%

Assessment Related Requirements

Some assignments require to use STATA which is 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 4 assignments to be submitted in groups throughout the course. The dates and submission guidelines will be announced on MyUni. Best 3 out of 4 marks will count toward the semester grade for this component. Because not all of these marks count for assessment, no special consideration will be given to students who do not submit their work (or submit it late) for medical, compassionate or any other reason. The projects are worth 30% of the final grade.

2. There will be one 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.
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 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.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

最新糖心Vlog