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ECON13-300: Advanced Econometrics

Description

Many types of economic and financial data naturally occur as a series of data points in temporal order. Stock market indices are a classic example of such time series. Standard statistical methods are not appropriate for such data. This subject provides an introduction to time series econometrics with an emphasis on practical applications to typical economic and financial issues. Emphasis will be placed on determining when it is appropriate to use the various time series econometrics techniques and the use of appropriate software to conduct the analysis.

Subject details

TypeUndergraduate
CodeECON13-300
EFTSL0.125
FacultyBond Business School
Semesters offered
  • September 2020 [Standard Offering]
  • September 2021 [Standard Offering]
Credit10
Study areas
  • Business and Commerce
Subject fees
  • Commencing in 2020: $4,340
  • Commencing in 2021: $4,390

Learning outcomes

1. Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
2. Apply linear and non-linear univariate techniques of time series models for business forecasts.
3. Analyse the statistical significance of stationarity of time series through unit root tests.
4. Critically analyse the theoretical and technical knowledge of Vector Autoregressive Models and Vector Error Correction models to establish and differentiate both short and long run relationships between the variables.
5. Demonstrate the advanced knowledge of unit roots and cointegration in the context of panel data regression models.
6. Demonstrate the ability to produce a written report that communicates ideas clearly, cogently, and thoroughly, using a professional style and format.
7. Demonstrate the ability to work effectively with others to successfully complete a project.

Enrolment requirements

Requisites: ?

Nil

Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Restrictions: ?

Nil

Subject outlines

Subject dates

Standard Offering
Enrolment opens14/07/2019
Semester start09/09/2019
Subject start09/09/2019
Cancellation 1?23/09/2019
Cancellation 2?30/09/2019
Last enrolment22/09/2019
Withdraw – Financial?05/10/2019
Withdraw – Academic?26/10/2019
Teaching census?04/10/2019
Standard Offering
Enrolment opens02/08/2020
Semester start14/09/2020
Subject start14/09/2020
Cancellation 1?28/09/2020
Cancellation 2?05/10/2020
Last enrolment27/09/2020
Withdraw – Financial?10/10/2020
Withdraw – Academic?31/10/2020
Teaching census?09/10/2020