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BX2122 - Econometrics and Big Data Analysis

Credit points: 3
Year: 2021
Student Contribution Band: Band 3
Administered by: College of Business, Law & Governance

In a day and age in which the availability of big data increases exponentially, the value of the skills to correctly analyse such data increases accordingly. This subject demonstrates a range of econometric models that can be used to interrogate or mine large datasets to test theories and ideas. The subject focuses on the application of these models, understanding their limitations and correctly interpreting their results. The subject provides valuable skills to students in economics, finance or any other discipline in which the analysis of big data is or will be important.

Learning Outcomes

  • formulate testable scientific hypotheses from economic theories;
  • conduct an econometric analysis (including specification tests) using real life big data and interpret the results of econometric models;
  • select an appropriate econometric model from a range of models to test hypotheses and interpret the results of these models.

Subject Assessment

  • Written > Examination (centrally administered) - (40%) - Individual
  • Written > Test/Quiz 1 - (30%) - Individual
  • Written > Research report - (30%) - Individual.
Prerequisites: BU1007 OR BU1807 OR BU1010 OR EC1101 OR MA1401 OR MA2401 AND BU1003 OR BU1903
Inadmissible
Subject
Combinations:
BX3022 EC2413 EC3413 EC5212 EC5216 BX3025 BX2225 EC5211 BX3122

Availabilities

Townsville, Internal, Trimester 3
Census Date 07-Oct-2021
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 36 hours - Combined Lectures; Tutorials/Workshops; Lecturer directed activities.
  • assessment and self-directed study

External, Trimester 3
Census Date 07-Oct-2021
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 36 hours online collaboration sessions - Online engagement
  • assessment and self-directed study
Method of Delivery: WWW - LearnJCU

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.