Year
2024Credit points
10Campus offering
No unit offerings are currently available for this unit.Prerequisites
10 cp from 200-level units in Economics
Incompatible
ECON304 - Applied Econometrics
Unit rationale, description and aim
Professional outcomes in the government, corporate sector and education sector require a workforce to be knowledgeable about data analysis techniques and possess advanced skills in analysing data using modern statistical packages. This unit will apply econometric principles in research methodology and in the evaluation of economic policy. Emphasis is placed on a hands-on analysis of real-world economic data using regression analysis and other techniques. The aim of this capstone unit is to introduce students to applied econometric methods and the stories told by data in their applications in economics, business, finance and government.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
| Learning Outcome Number | Learning Outcome Description | Relevant Graduate Capabilities |
|---|---|---|
| LO1 | Identify econometric methods and their applications in economics, business, finance and government | GC2, GC3, GC4 |
| LO2 | Specify and estimate models, test hypotheses, interpret results and make economic forecasts | GC2, GC3, GC4 |
| LO3 | Formulate policy and evaluate the impact of economic policies, interpret economic concepts and their application to contemporary issues | GC1, GC3, GC4, GC5 |
| LO4 | Solve problems using readily available econometric computer software and commercial databases | GC2, GC3, GC4 |
| LO5 | Autonomously produce a research report that effectively communicates the results of an investigation using applied econometrics | GC2, GC3 |
Content
Topics will include:
- Descriptive statistics;
- An introduction to probability;
- Sampling and sampling distribution;
- Confidence interval estimation;
- Hypothesis testing;
- Chi-square test and analysis of variance;
- Principles of regression analysis;
- The use of dummy variables;
- The problems of serial correlation, heteroscedasticity and multicollinearity;
- Analysing Indigenous data;
- Statistical inference in regression analysis;
- Advanced regression analysis;
- Techniques in using statistical analysis software.
Learning and teaching strategy and rationale
This unit is designed to be offered fully online and will include synchronous delivery of unit content, collaborative online learning activities and other technology-enabled learning synchronous and asynchronous learning opportunities to foster interaction between students.
To achieve the learning objectives outlined above, this unit engages students in active learning activities, such as reading, writing, discussion, and problem-solving to promote analysis, synthesis, and evaluation of economic data based on the applied methods of statistical analysis. This unit comprises a series of online lectures (a theoretical component of the course) and online tutorials (practical hands-on sessions). During tutorials, students obtain critical thinking analytical skills through solving problem sets and discussing case studies. An essential part of their econometrics training is obtaining practical skills through working with statistical software packages in analysing data.
Students undertake practical project-based learning with the goal of developing their analytical, problem solving, decision making and investigative skills with regard to the analysis of real-world economic and financial data. During tutorials, students obtain critical thinking analytical skills through solving problem sets and discussing case studies. An essential part of their econometrics training is obtaining practical skills through working with statistical software packages in analysing data.
This is a 10-credit point unit and has been designed to ensure that the time needed to complete the required volume of learning to the requisite standard is approximately 150 hours in total across the semester. To achieve a passing standard in this unit, students will find it helpful to engage in the full range of learning activities and assessments utilised in this unit, as described in the learning and teaching strategy and the assessment strategy. The learning and teaching and assessment strategies include a range of approaches to support your learning such as reading, reflection, discussion, webinars, podcasts, videos etc.
Assessment strategy and rationale
The assessments in this unit encourage students to undertake practical project-based learning to develop the skills required to locate and analyse real-world economic and financial data (both qualitative and quantitative) using statistical methods and modern data analysis software. Such skills are highly regarded in economics graduates by potential employers. The assessment tasks have been developed in order to achieve the learning objectives outlined above and enable the development of specific critical thinking, data analysis, and data presentation skills that are required by the industry. The unit will cover a wide range of assessment tasks including:
1. Skill development weekly activities are designed for students to (1) obtain fundamental content knowledge, and (2) gain practical experience in the field through solving problems, discussing case studies, and mastering the use of statistical software.
2. Research assignment provides the opportunity to undertake research into the scholarship on a specific topic (from data collecting, coding and cleaning, analysis of the data using statistical techniques and appropriate software packages), and to develop further communication and writing skills based on the results of data analysis.
3. Final exam. This assessment is focusing on testing students’ skills and application of relevant theoretical knowledge that students have acquired through the course. The final exam will require students to analyse one or more case studies based on an output file from the statistical software package. Students will also apply their theoretical statistical knowledge to real-world data and solve realistic, complex, and contextually rich situations that might involve designing policy implications. The task may include multiple case studies and be delivered as central examination, school-based examination, or take-home examination.
Overview of assessments
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes |
|---|---|---|
Assessment Task 1: Problem sets These are designed to build skills of finding economic and financial data, downloading the data, cleaning it, and analysing using Excel and open source programming languages such as R and Python. | 30% | LO1, LO2 |
Assessment Task 2: Research assignment The purpose of this practical assignment is to develop students’ skills to locate, obtain, clean, organise economic and financial data and apply theoretical modelling knowledge to the analysis of the data. Students will be required to use a statistical software package for their analysis. Based on the results of the analysis, they will be required to document their results in the report. | 30% | LO2, LO3, LO4, LO5 |
Assessment Task 3: Final Exam The final exam will test students’ knowledge and understanding of the different topics covered in the unit. Students will also receive an output file from the statistical software package and will be required to apply their knowledge and critical thinking skills to analyse the output and provide an evaluation of a particular policy. | 40% | LO1, LO2, LO3 |
Representative texts and references
Asteriou, D., and Hall, S.G. (2015). Applied Econometrics (3rd ed.). Palgrave-Macmillan.
Brooks, C. (2019). Introductory Econometrics for Finance. Cambridge University Press.
Crooksa, K., Carlsona, S., and Daltona, C. (2019). Defining, controlling and analysing Indigenous data: commitment to historical consistency or commitment to Australian Aboriginal and Torres Strait Islander peoples? Journal of the SAX Institute, 29(4), available online https://www.phrp.com.au/wp-content/uploads/2019/12/PHRP2941926.pdf
Enders, W. (2015). Applied Econometric Time Series. (4th ed.). Wiley.
Gujarati, D. (2014). Econometrics by Example. Palgrave Macmillan.
Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. (2020). Introduction to Econometrics with R. Available online: econometrics-with-r.org.
Hill, R.C, Griffiths, W.E., and Lim, G.C. (2018). Principles of Econometrics (5th ed.). Wiley.
Martin, V., Hurn S., and Harris, D. (2013). Econometric Modelling with Time Series: Specification, Estimation and Testing. Cambridge University Press.
Navarro, D. (2020). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners. Available online: https://learningstatisticswithr.com/lsr-0.6.pdf
Stock, J. and Watson, M. (2015). Introduction to Econometrics (3rd ed.). Addison Wesley Longman, Boston.
Studemund, A. H. (2017). Using Econometrics: A Practical Guide (7th ed.). Pearson.
Wooldridge, J.M. (2015). Introductory Econometrics: A Modern Approach (6th ed.). South-Western College Publishers.
Further references
Australian Bureau of Statistics: www.abs.gov.au
Reserve Bank of Australia: www.rba.gov.au
The Comprehensive R Archive Network, https://cran.r-project.org/
R Studio: https://www.rstudio.com/