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EC5216 - Data Collection, Analysis and Interpretation in Economics

Credit points: 3
Year: 2019
Student Contribution Band: Band 3
Administered by: College of Business, Law & Governance

This subject demonstrates how to use 'real' data in a variety of different econometric models to test economic theories and ideas. Its focus is on using techniques and understanding their limitations and their results (rather than on understanding econometric theory). Starting with the basic problem of determining how to turn an interesting question/issue into a testable hypothesis, it provides an overview of the types of data often used by economists and discusses techniques for collecting primary data where necessary. It introduces a range of different econometric models, identifying when such models are, or are not appropriate, and explains how to gauge the 'accuracy' of the models and to interpret outputs/results. The subject is tailored to students who (envision to) work at a position that requires advanced economics skills.

Learning Outcomes

  • Understanding how to identify key variables for use in economic models and how to collect data for use as proxies in those models;
  • Demonstrate a critical awareness of the way in which economic theories can be re-framed as testable scientific hypothesis;
  • Demonstrate extensive familiarity with how to use a range of different econometric models to test economic theories;
  • Demonstrate an advanced understanding of how to assess the appropriateness of different econometric models given the type of data available, and the economic theory being explored;
  • Equip students with more advanced skills to interpret the output of econometric models.
Inadmissible
Subject
Combinations:
BX3122 BX3022

Availabilities

Townsville, Internal, Study Period 1
Census Date 28-Mar-2019
Coord/Lect: Dr Rabiul Beg.
Workload expectations:

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

  • 26 hours lectures
  • 12 hours tutorials
  • 12 hours workshops/Seminars
  • assessment and self-directed study
Assessment: end of semester exam (50%); case study (30%); mid-term exam (20%).

External, Study Period 1
Census Date 28-Mar-2019
Coord/Lect: Dr Rabiul Beg.
Workload expectations:

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

  • 0 hours
  • assessment and self-directed study
Method of Delivery: WWW - LearnJCU
Assessment: end of semester exam (50%); case study (30%); mid-term exam (20%).

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.