ECON 2515 - Intermediate Applied Econometrics II

North Terrace Campus - Semester 1 - 2021

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 2515
Course	Intermediate Applied Econometrics II
Coordinating Unit	Economics
Term	Semester 1
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week
Available for Study Abroad and Exchange	Y
Prerequisites	ECON 1008 or ECON 1011 or equivalent
Incompatible	ECON 2504
Assumed Knowledge	ECON 1005 and ECON 1012
Restrictions	Not suitable for BEc(Adv) students
Assessment	Typically assignments, mid-term test and final exam

Course Staff

Course Coordinator: Dr Nadya Baryshnikova

Email: patricia.sourdin@adelaide.edu.au

Office location: Nexus 10, TBA

Office hours: by appointment

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. Estimate simple and multiple linear regressions with quantitative data
3. Test and correct for heteroscedasticity
4. Estimate linear regressions with qualitative data
5. Interpret outcomes of the regressions
6. Discuss and communicate methodology and results in a team

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)	1-5
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	3,5
Teamwork and communication skills developed from, with, and via the SGDE honed through assessment and practice throughout the program of studies encouraged and valued in all aspects of learning	6
Career and leadership readiness technology savvy professional and, where relevant, fully accredited forward thinking and well informed tested and validated by work based experiences	5

Learning & Teaching Modes

Online 2 hours of weekly lectures (may be split into smaller units) and weekly face-to-face 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 communication software for your group assignment meetings.

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
1-2	Module1- Nature of Econometrics and Economic Data 1.1- What is econometrics? 1.2- Steps in empirical economic analysis 1.3- The structure of economic data 1.4- Graphing data 1.5- Causality and the notion of Ceteris Paribus in econometric analysis
3-4	Module 2- The Simple Linear Regression Model 2.1- Definition of the simple linear regression model 2.2- Deriving the ordinary least square estimates 2.3- Examples of simple regression obtained using real data 2.4- Properties of OLS 2.5- Unit of measurement and functional form 2.6- Unbiasedness, consistency and vraiances of the OLS estimates
5-6	Module 3- Mulitple Linear Regression Model: Estimation 3.1- Motivation 3.2- Mechanism and interpretation of ordinary least square equation (OLS) 3.3- Properties of OLS estimators
7	MidTerm Test
8-9	Module 4- Multiple Linear Regression Model: Inference 4.1- Sample distribution of the OLS estimators 4.2- Testing hypotheses about a single population parameter: The t-test 4.3- Confidence intervals 4.4- Testing hypotheses about a single linear combination of the parameter 4.5- Testing multiple linear restrictions: The F-test 4.6- Confidence intervals for predictions 4.7- Reporting regression results
10	Module 5- Heteroscedasticity and Autocorrelation 5.1- Definition of heteroscedasticity 5.2- Testing for heteroscedasticity 5.3- Correcting heteroscedasticity
11-12	Module 6- Heteroscedasticity and Autocorrelation 6.1- Describing qualitative information 6.2- A single dummy independent variable 6.3- Using dummy variables for multiple categories 6.4- Interactions involving dummy variables 6.5- A binary dependent variable: The linear probability model (LPM)

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 / 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 may utilise the online forum of MyUni.

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
Assignments	Group	30%	1-6
Midterm	Individual	20%	1-5
Final	Individual	50%	1-5
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. No individual work will be accepted for this component. 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 dates and submission guidelines will be announced on MyUni. 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 tests will be added to the weighting of the final exam. If students choose to sit the tests, they will no longer be redeemable and the test score will count towards final grade in the course.

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

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