ECON 2504 - Intermediate Econometrics II

North Terrace Campus - Semester 1 - 2017

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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Course Details

Course Code	ECON 2504
Course	Intermediate Econometrics II
Coordinating Unit	Economics
Term	Semester 1
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week. Intensive (up to 6 hours per week) in Summer Semester.
Available for Study Abroad and Exchange	Y
Prerequisites	ECON 1008 or ECON 1011 or STATS 1000 or equivalent
Incompatible	ECON 2006 & STATS 2002, STATS 2003 & MATHS 2103
Assumed Knowledge	ECON 1004, ECON 1000, Maths as taught in ECON 1005
Restrictions	Not suitable for BCompSc, BCompGr or BEng(Software Engineering) students
Assessment	Typically group and individual assignments, tests and final exam.

Course Staff

Course Coordinator: Dr Nadya Baryshnikova

Summer School and Semester 2
Course Coordinator: Dr Patricia Sourdin
Office hours: TBA
Office location: TBA
Telephone: TBA

Semester 1
Course Coordinator: Dr Nadya Baryshnikova
Email: nadezhda.baryshnikova@adelaide.edu.au
Office hours: TBA
Office location: Nexus 10, Level 4, Room 4.04
Telephone: 8313 4821

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:

Conduct basic statistical and econometric analysis.
Explain and interpret econometric results.

最新糖心Vlog Graduate Attribute	Course Learning Outcome(s)
Deep discipline knowledge informed and infused by cutting edge research, scaffolded throughout their program of studies acquired from personal interaction with research active educators, from year 1 accredited or validated against national or international standards (for relevant programs)	1,2
Critical thinking and problem solving steeped in research methods and rigor based on empirical evidence and the scientific approach to knowledge development demonstrated through appropriate and relevant assessment	1,2
Career and leadership readiness technology savvy professional and, where relevant, fully accredited forward thinking and well informed tested and validated by work based experiences	1,2

Learning & Teaching Modes

Classes will meet for 2 hours a week for the lecture and once for a 1-hour tutorial depending on the time slot students sign up for. Office hours with the lecturer and tutors will be announced in due course. Please adhere strictly to the designated office hours.

Workload

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 three hours of lectures and tutorials each week, which means that students should undertake nine hours of self-study each week of the teaching term.

Homework assignments are issued each week. Students are expected to work in groups on these assignments. In addition to the weekly group questions there will be two assignments to be submitted individually for a grade. All students may be asked to present their solutions during each tutorial session.

Learning Activities Summary

Teaching & Learning Activities	Related Learning Outcomes
Lectures	1,2
Tutorials	1,2

TENTATIVE LECTURE SCHEDULE (subject to change)

Topics	Title	Chapters
Topic 1	Introduction to Econometrics	Chapter 1 (HGL)
Topic 2	Introduction to Basic Statistics and Probability	Probability Primer
Topic 3	The Simple Linear Regression Model	Chapter 2
Topic 4	Interval Estimation and Hypothesis Testing	Chapter 3
Topic 5	Prediction, Goodness of Fit and Modeling Issues	Chapter 4
Topic 6	The Multiple Linear Regression Model	Chapter 5
Topic 7	Further Inference in the Multiple Regression Model	Chapter 6
Topic 8	Using Indicator Variables	Chapter 7
Topic 9	Heteoskedasticity	Chapter 8

Specific Course Requirements

Homework completion may require access to STATA. If you do not have STATA at home, you may use the computer labs on campus. Please refer to for further details.

For course related questions, students are encouraged to utilise the designated office hours of the lecturer and the tutors or post their questions on the discussion board. Questions over the telephone and email are strongly discouraged.

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	Due Date/ Week	Weight	Learning Outcomes
Homework Assignments (in groups)	Week TBA	20%	1,2
Individual Homework Assignments	Week TBA	30%	1,2
Small Research Project	Week TBA	5%	1,2
Final Exam	Week TBA	45%	1,2
Total		100%

Assessment Related Requirements

Assessment Detail

1. Homework will be posted each week. The students will be asked to form groups at the beginning of the course and do the exercises in these groups. 5 of these homeworks will be graded, the rest are not graded. The homeworks that will be graded will be announced on the course page in advance and will have a clearly marked due date. No individual work will be accepted for this component. The group homework is submitted online. The tutor will mark one question of his/her choice in each graded homework. At the end of the course, best 3 out of 5 marks will count toward the semester grade for this component.

2. There will be 2 homework assignments to be submitted individually throughout the course. The dates and submission guidelines will be announced on the course page. At the end of the course, best 1 out of 2 marks will count toward the semester grade for this component.

3. Small Research Project: This is mainly an in-tute project. There will be two Small Research Project sessions during tutorials (most likely in weeks 9 and 10). The dates will be announced on the course page closer to the time. Activity will be assessed at the end of the session. Missed sessions will count as 0. If you cannot attend your tutorial session at that time, please come to any other tutorial during that week. If you are late by more than 10 minutes, you will be marked as absent (you may be able to make up that session by attending another session during that week, if it is still available).

Unless there are valid reasons and documentation, missed homework, projects, tests or examinations will be graded 0. Please refer to the Modified Arrangements for Coursework Assessment Policy (and the Schedule to the Policy)
for further details about eligibility and application forms. Missed or late submissions of any assignments will not be accepted and will be graded 0.

Submission

Submission of the assignments is required as per instructions on the course web page.

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 .

Additional Assessment

If a student receives 45-49 for their final mark for the course they will automatically be granted an additional assessment. This will most likely be in the form of a new exam (Additional Assessment) and will have the same weight as the original exam unless an alternative requirement (for example a hurdle requirement) is stated in this semester’s Course Outline. If, after replacing the original exam mark with the new exam mark, it is calculated that the student has passed the course, they will receive 50 Pass as their final result for the course (no higher) but if the calculation totals less than 50, their grade will be Fail and the higher of the original mark or the mark following the Additional Assessment will be recorded as the final result.

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.

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

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