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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

Type: Undergraduate Subject
Code: ECON13-300
EFTSL: 0.125
Faculty: Bond Business School
Semesters offered:
  • September 2024 [Standard Offering]
Credit: 10
Study areas:
  • Business, Commerce, and Entrepreneurship
Subject fees:
  • Commencing in 2023: $4,050.00
  • Commencing in 2024: $4,260.00
  • Commencing in 2025: $4,460.00
  • Commencing in 2023: $5,400.00
  • Commencing in 2024: $5,730.00
  • Commencing in 2025: $5,990.00

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.

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:

Subject dates

  • Standard Offering
    Enrolment opens: 14/07/2024
    Semester start: 09/09/2024
    Subject start: 09/09/2024
    Cancellation 1: 23/09/2024
    Cancellation 2: 30/09/2024
    Last enrolment: 22/09/2024
    Withdraw - Financial: 05/10/2024
    Withdraw - Academic: 26/10/2024
    Teaching census: 04/10/2024
Standard Offering
Enrolment opens: 14/07/2024
Semester start: 09/09/2024
Subject start: 09/09/2024
Cancellation 1: 23/09/2024
Cancellation 2: 30/09/2024
Last enrolment: 22/09/2024
Withdraw - Financial: 05/10/2024
Withdraw - Academic: 26/10/2024
Teaching census: 04/10/2024