ECON 7202 - Advanced Econometrics V

North Terrace Campus - Semester 2 - 2015

In this course we study advanced micro and time series econometrics topics that are not covered in Econometrics IV and Advanced Time Series Econometrics IV or those topics discussed in these two courses in more detail. Topics can include bootstrap, generalized method of moment (GMM), empirical likelihood (EL), instrument variables (IV) estimation, maximum likelihood estimation (MLE), panel data methods (basic models, dynamic panel model, panel model with limited dependent variable), limited dependent variable models, sample selection corrections, duration model, autoregressive conditional heteroscedasticity (ARCH), generalized ARCH (GARCH), Kalman filter, regime switching model, stochastic calculus, diffusion models, and financial economics.

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Course Details

Course Code	ECON 7202
Course	Advanced Econometrics V
Coordinating Unit	Economics
Term	Semester 2
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Contact	Up to 4 hours per week
Available for Study Abroad and Exchange	Y
Prerequisites	A minimum of a Credit in ECON 3023 or ECON 3507 or ECON 7204 or equivalent
Assumed Knowledge	Basic matrix algebra, basic Matlab and STATA
Assessment	Typically homework & final exam; Sometimes paper and presentations

Course Staff

Course Coordinator: Dr Terence Cheng

Location: Room 4.06, Nexus 10 Tower
Telephone: 8313 1175
Email: terence.cheng@adelaide.edu.au

Course Timetable

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

Course Learning Outcomes

The outcomes of this course are:

1	To learn various advanced econometric methods, estimation methods and related econometric theories
2	To apply these methods to data or econometric modelling techniques
3	Students are expected to be able to write a code in Stata to estimate econometric models and replicate results from published econometrics research
4	Students are expected to be able to use Stata, Eviews, and etc, to estimate econometric models using real world data
5	Students are expected to be able to interpret econometric estimates, analyse the results and critically evaluate published econometric research.

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.	2,3,5
An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems.	2,5
Skills of a high order in interpersonal understanding, teamwork and communication.	5
A proficiency in the appropriate use of contemporary technologies.	2,3,4
A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life.	2,3,5

Required Resources

Textbooks

1	A.C Cameron and P.K. Trivedi	Microeconometrics: Methods and Applications	Cambridge 最新糖心Vlog Press, 2005
2	J. Angrist and J.S. Pischke	Mostly Harmless Econometrics	Princeton 最新糖心Vlog Press, 2009

Computer Software

Stata

Available on the computers in Honours student room, PhD student room, and the computer lab (10 Pulteney St. 2.20 Computer Suite 3 only)

NB: Students are encouraged to use software other than the programs listed here. However, they must ensure that the software is appropriate for their project. Students who use computers connected to the 最新糖心Vlog network can make a request with ITS to install Matlab on their machines.

Recommended Resources

i)	J.M. Wooldridge	Econometric Analysis of Cross Section and Panel Data	2nd Edition, MIT Press, 2010
ii)	A.C. Cameron and P.K. Trivedi	Microeconometrics Using Stata Revised Edition	Stata Press, College Station: TX, 2009
iii)	Train, K.E.	Discrete Choice Methods with Simulation	Cambridge 最新糖心Vlog Press, 2003
iv)	W. H. Greene	Econometric Analysis	7th Edition, Pearson 5 & 6 Ed. Prentice Hall, 2003
v)	R. Winkelmann and S. Boes	Analysis of Microdata	2nd Edition, Springer, 2009
vi)	Gould W., J. Pitblado and W. Sribney	Maximum Likelihood Estimation with Stata	Third Edition, Stata Press, College Station: TX, 2006

Online Learning

1	E-mail	Check your student email often as course-related announcements are communicated via email
2	MyUni	Course materials will be posted on the MyUni course webpage,

Learning & Teaching Modes

1	Lecture slides
2	Tutorial exercises
3	Computer exercises and program codes
4	Textbooks
5	Journal articles

Workload

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

All students in this course are expected to attend all lectures, workshops and labs throughout the semester.

Lecture notes	2 hours/week
Additional readings	2 hours/week
Problem solving and computer exercises	2 hours/week

NB: The above guide is for private study, that is, study outside of your regular classes.

Learning Activities Summary

a)	Causal Models and Treatment Evaluation
b)	Models for Cross-Section Data: Discrete and Limited Dependent Variables; Mixture Models.
c)	Models for Panel Data: Linear and Dynamic Panels; Non-Linear Panels. Missing Data
d)	Maximum Likelihood (ML) using Stata; Simulation-Based ML Estimation
e)	Programming and Data Management using Stata

Specific Course Requirements

N/A

Small Group Discovery Experience

N/A

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
Homework and computer exercises.	Problem solving and computer exercises	10%
Midterm Exam: see Assessment Detail	Formative, problem solving and computer exercises	30%
Final Exam	Formative, problem solving and computer exercises	60%

Assessment Related Requirements

N/A

Assessment Detail

Homework and computer exercises	Assignments will be made available on MyUni and distributed in the tutorials the teaching week before they are due. They need to be handed in at the beginning of the lecture the week they are due. Late assignments will be accepted only if accompanied by appropriate documentation. Assignments consist of a mix of paper-and-pencil and software exercises, and would involve reading a journal article from the literature.
Midterm Exam	Mid-term examination containing short answer and problems/computational questions. There will be no supplementary exam for the midterm exam. If you miss this exam and you provide a medical certificate or compassionate reasons, your final exam will account for 90% (instead of 60%) of your total mark. The date will be posted on MyUni and discussed with students in lectures.
Final Exam	Final examination containing short answer and problems/computational questions. The date will be posted on MyUni and discussed with students in lectures.

Submission

After being marked, generally, the assessment will be returned to students in class about a week after submission.

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

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