23571 Introductory Econometrics
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): (23115 Economics for Business OR 23000 Principles of Microeconomics)) AND (((26134 Responsible Evidence-Based Decisions OR 33130 Mathematics 1 OR 33230 Mathematics 2)) OR (36200 Arguments, Evidence and Intuition AND 95728 Introduction to Health Statistics)
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 25571 Introductory Econometrics
Description
Introductory Econometrics equips students with a general knowledge of regression analysis and model building, which stands them in good stead for basic empirical work in business environments. In particular, students are able to quantify the effects of causal variables and predict using regression models. The approach to modelling, and the reasoning about multi-variable empirical relationships, strengthens students' analytic skills.
Subject learning objectives (SLOs)
| 1. | derive least squares estimators and their properties |
|---|---|
| 2. | build multiple regression models using real-world data |
| 3. | interpret multiple regression coefficients and conduct inference |
| 4. | explain the properties of regression models with different functional forms. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the following program learning objectives:
- Apply technical and professional skills to operate effectively in business (4.1)
Contribution to the development of graduate attributes
Introductory Econometrics will equip students with a general knowledge of least squares estimation and model building, which will stand them in good stead for basic empirical work in business environments. In particular, students are able to quantify the effects of causal variables and predict using regression models. The approach to modelling, and the reasoning about multi-variable empirical relationships, strengthens students' analytic skills. As the first subject in the econometrics sub-major, it provides students with the analytic tools required for further study in time series and cross-sectional econometrics.
This subject contributes to the development of the following graduate attributes:
- Professional and technical competence
Teaching and learning strategies
The course will be taught using a combination of lectures and tutorials. The lectures cover the core concepts supplemented by applications. The tutorials consist of in-depth discussions of various topics, and laboratory sessions using statistical software. Details of our teaching and learning strategies are described below.
Course structure: Although I have stressed the importance of logical thinking, I will also back up most theoretical concepts with examples and applications (whenever feasible) to facilitate the delivery of materials. However, due to time constraints, it is impractical to go over all the examples in lectures. The relevant chapters in the textbook play an important role in filling this gap. The lectures will, on average, consist of 70 percent theory and 30 percent applications. The tutorials are very important in enhancing your understanding of the theory. They will cover additional examples and applications in greater detail.
Statistical software: Knowledge of statistical software is an object of assessment and is needed to solve tutorial problems and assignments. This course will use two software: EViews and Excel. The tutorial in week 3 will provide a walkthrough of EViews. EViews is available in the tutorial rooms.
Activities, course materials and other resources: Lecture slides, tutorial solutions, and tutorial problems for the following week will be uploaded to the UTS Learning Management System prior to the lecture. Prior to each class, you will be responsible for printing out the course materials and bringing them to lectures and tutorials. Although tutorial problems are not part of the assessment criteria, they are very important for exam preparation. You should try solving the tutorial problems before attending the laboratory sessions. During the sessions, there will be in-depth discussions of these problems with the aid of statistical software (wherever applicable). The discussions can be held between the tutor and the students, or among the students in the form of collaborative learning activities. Through these activities, students will receive feedback on their learning from the tutor and their peers.
To enhance your understanding of the course material, I have selected additional practice problems from end-of-chapter exercises in the textbook. Although these are optional exercises, I encourage you to attempt the problems following each class. This activity will prepare you well for the upcoming classes. The questions and answers will be uploaded to the learning management system at the same pace as the lectures. To maximize the benefit, look at the answers only AFTER you attempt the questions.
All course announcements will be made via the learning management system.
Whenever you have questions, you are strongly encouraged to come to our consultation hours. Our past experiences have shown that students benefited tremendously from the use of this resource.
Content (topics)
- Data structure and econometric modelling
- Basic properties of ordinary least squares
- Inference and hypothesis testing
- Multiple regression analysis: estimation and inference
- Multiple regression analysis: generalizations and alternative functional forms
- Heteroskedasticity and model specification issues
- Introduction to qualitative response models
Assessment
Assessment task 1: Quizzes (Individual)*
| Objective(s): | This addresses subject learning objective(s): 1, 3 and 4 |
|---|---|
| Weight: | 10% |
| Criteria: |
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 2: Assignments (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3 and 4 This addresses program learning objectives(s): 4.1 |
|---|---|
| Weight: | 40% |
| Criteria: |
|
Assessment task 3: Final Exam (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3 and 4 |
|---|---|
| Weight: | 50% |
| Criteria: |
|
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Stock J and M Watson (2020). Introduction to Econometrics, Pearson, global edition.
(A copy of the 4th edition (2019) is available in UTS library. Consider purchasing the book as we follow it closely. It has a wealth of additional pen-and-paper and empirical exercises.)
References
1. Gujarati D and D Porter (2009). Basic Econometrics, McGraw-Hill, 5th edition.
(This book was the previous textbook of 23571. It is a rigorous but technical book on most topics we cover. A copy is available in UTS library.)
2. Gujarati D and D Porter (2010), Essentials of Econometrics, McGraw-Hill.
(This book contains an easier treatment of most topics that we cover. Multiple copies are available in UTS library.)
3. Gujarati D (2015), Econometrics by Example, Macmillan.
(This book contains a lot of examples on econometrics. Multiple copies (including an earlier version) are available in UTS library.)
4. Jeffrey M. Wooldridge (2009). Introductory Econometrics: A Modern Approach, Thomson South-Western.