Data Analysis and Econometrics
Duration
- One semester or equivalent
Contact hours
- 24 hours face to face + blended
On-campus unit delivery combines face-to-face and digital learning.
Aims and objectives
This unit is designed so that students learn fundamental techniques of data analysis, basic econometric methods and learn to use data to solve real-world problems by estimating relevant parameters (such as elasticities, marginal values etc). Students acquire expertise in applying data analysis and econometric methods, including regression analysis and its extensions, to various types of data. Students also learn how to use econometrics to test theory, analyse economic and business behaviour, and assist in policy formation. The subject is application orientated and practical work is performed using statistical software.
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
1. Demonstrate an understanding of important econometric methods and interpret estimation results.
2. Analyse the concepts of model specification and diagnostic testing procedure in time series and cross sectional econometrics.
3. Solve problems using econometric computer software and commercial databases.
4. Work in groups to solve econometric problems and clearly communicate their results and interpretations.
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
On Campus Class | 2 | 12 | 24 |
Online Contact Learning Activities | 1 | 12 | 12 |
Unspecified Activities Independent Learning | 9.5 | 12 | 114 |
TOTAL | 150 hours |
OUA
Type | Hours per week | Number of Weeks | Total |
Online Contact Learning Activities | 12.5 | 12 | 150 |
TOTAL | 150 hours |
Assessment
Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
Presentation | Group | 10-20% | 1,2,3,4,5 |
| Assessment | Individual | 20-30% | 1,2 |
| Project Report | Group | 10-20% | 1,2,3,4 |
| Examination | Individual | 40-60% | 1,2 |
Content
- Linear Regression
- Interval Estimation and Hypothesis Testing
- Prediction, Goodness of Fit and Modelling Issues
- Multiple Regression Model
- Further Inference in the Multiple Regression Model
- Nonlinear relationships
- Heteroskedasticity
- Dynamic models, autocorrelation and forecasting
- Non-stationary time series data and co-integration
- Qualitative and limited dependent variable models
Study resources
- Reading materials.
Reading materials
Students are advised to check the unit outline in the relevant teaching period for appropriate textbooks and further reading.