ECON 2504 - Intermediate Econometrics II

North Terrace Campus - Semester 2 - 2015

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. 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.

Open All

Course Details

Course Code	ECON 2504
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. Intensive in Summer Semester.
Available for Study Abroad and Exchange	Y
Prerequisites	ECON 1008 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
Assessment	Typically, tutorial participation &/or exercises, assignments, tests & final exam

Course Staff

Course Coordinator: Dr Nadya Baryshnikova

Summer School
Course Coordinator: Dr Nicholas Sim
Office hours: TBA
Office location: Nexus 10, Level 4, Room 4.46
Telephone: 8313 4927

Semester 1 & 2
Course Coordinator: Dr Nadya Baryshnikova
Email: nadya.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

The course aims to achieve the following outcomes. It will provide students:

1. with knowledge on the fundamentals of econometrics and its application

2. with knowledge and proficiency on the use of statistical packages for econometric and statistical analysis

3. with the ability to conduct independent data analysis and inquiry using the tools of statistics and econometrics

最新糖心Vlog Graduate Attribute	Course Learning Outcome(s)
Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised.	1,2
The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner.	1,2
An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems.	1,2
A proficiency in the appropriate use of contemporary technologies.	2,3

Learning & Teaching Modes

Classes will meet three times a week: twice for a 1-hour lecture and once for a 2-hour tutorial depending on the time slot students sign up for.

Office hours to be announced. 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 four hours of lectures and tutorials each week, which means that students should undertake eight hours of self-study each week of the teaching term.

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

Learning Activities Summary

The tentative outline of the course (subject to change) is:

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. Questions over the telephone are strongly discouraged.

Small Group Discovery Experience

There will be a Small Group Discovery Experience during two of the tutorials (most likely in Week 5 and Week 10). All students are expected to attend and participate.

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

Students will be assessed based on the following:

Tutorial assignments (in groups) 5%
Homework assignments 30%
Small Group Discovery 5%
Final examination 60%
Unless instructed otherwise, students are permitted to bring in an A4 size cheat sheet with both sides written for the final exam.

Assessment Related Requirements

1 - Attendance in class and tutorials is required.
2 - To gain a pass, a mark of at least 45% must be obtained on the final examination as well as a total of at least 50% overall.

Assessment Detail

1. Tutorial homework will be posted each week. The students will be asked to form groups at the beginning of the course and do the exercusesin these groups. No individual work will be accepted for this component. The group homework will be collected in tutorials. The tutor will mark one question of his/her choice. At the end of the course, best 8 out of 11 marks will count toward the semester grade for this component.

2. There will be 3 homework assignments to be submitted individually throughout the course. The dates and submission guidelines will be announced on MyUni. At the end of the course, best 2 out of 3 marks will count toward the semester grade for this component.

3.Small Group Discovery: There will be two Small Group Discovery sessions during tutorials (most likely in weeks 5 and 10). Please check the dates on MyUni. Activity will be assessed at the end of the session. Missed sessions will count as 0. If you cannot attend your Small Group Discovery 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 documentations, missed test or examination 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 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 .

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.

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