TRADE 7012 - Trade Statistics
North Terrace Campus - Trimester 1 - 2022
General Course Information
Course Code TRADE 7012 Course Trade Statistics Coordinating Unit Institute for International Trade Term Trimester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 36 hours Available for Study Abroad and Exchange Y Course Description Trade Statistics is a course designed to assist students in the analysis and application of international trade and economic statistics in the postgraduate programs of the Institute for International Trade. The course will review and revisit the principles of international trade from an applied perspective. It will show how robust data and statistics play an important role in decision making in economic and trade policy. The concept of comparative advantage will be discussed, the relationship between trade and economic growth analysed and the impact of trade barriers highlighted. The course will also focus on statistical concepts relevant to international trade and economic analysis. Students will be introduced to several publicly available economic and trade databases and their relevance. The course will provide students with the theoretical framework for trade and economic analysis. This will include, but not be limited to, the theoretical foundations of the general equilibrium models and partial equilibrium models such as the gravity model of international trade. The course will provide students with the tools and skills to undertake their own trade statistical research. Students will be introduced to several statistical methods such as trade indicator analysis, trade preference indicators, trade diversion and trade creation and the gravity model to international trade. Activities of this course include the evaluation of trade-related economic reports and a class exercise using online access to trade data sets to develop a trade strategy for countries negotiating trade agreements.
No information currently available.
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
On successful completion of this course, students will be able to:
1 Identify statistical data from economic data bases; 2 Apply relevant statistical techniques for the analysis of trade and economic data; 3 Apply foundation knowledge of international trade and economic resources and research techniques to successfully analyse trade and economic data; 4 Analyse international trade and economic data for trade policy recommendations; 5 Construct efficient statistical trade and economic policy research, using skills associated with effective electronic databases and trade statistical analysis; and 6 Apply effective writing, research and presentation skills in the construction of policy argument and analysis on international trade issues.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1, 2, 3, 4, 5, 6
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
- Learning Resources
Learning & Teaching Activities
Learning & Teaching ModesThe Learning & Teaching modes of this course will comprise of a mix of online and face-to face modules. These will include group work and presentations, discussions and debate.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The Institute requires students undertaking this course to attend all face-to-face modules and to successfully complete all online/e-modules. This course comprise of approximately 36 contact hours (structured learning). In addition to time spent in class, students are expected to devote an additional 120 non-contact hours to study and research work in this course as well as to successfully complete online/e-modules.
Learning Activities Summary
The course will be delivered through a mix of three face-to-face sessions (Modules) and online modules.
- Review of Excel tools
- Introduction to statistical methods
- Introduction to trade economic analysis and tools
- Introduction to trade indicator tools and analysis
- Review and enhancement of trade economic analysis and tools
- Review and enhancement of trade indicator tools and analysis
- Introduction to regression analysis and stata
- Regression analysis, time series analysis and forecasting techniques
- Demand for trade analysis
- Gravity model to international trade
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Task Type Due Weighting Learning Outcome
(refer to MyUni)
14 March 2019
5% 1,2 Data presentation Formative
16 March 2019
5% 1,2 Data analysis (Part A) Presentation Formative 5 April 2019 5% 1,2,3,4 Date analysis (Part A): Presentation - Peer assessment Formative 5 April 2019 5% 1,2,3,4 Date analysis (Part A): Report/Policy brief Formative 25 April 2019 25% 1,2,3,4 Data analysis (Part B) Presentation Summative 3 May 2019 10% 1,2,3,4,5 Date analysis (Part B): Presentation - Peer assessment Summative 3 May 2019 5% 1,2,3,4,5 Date analysis (Part B): Report/Policy brief Summative 10 May 2019 40% 1,2,3,4,5 Total 100%
Assessment DetailPre-Face-to-Face Activity (5%)
Students are to contribute to a discussion post set on the MyUni course page.
Data presentation (5%)
Students will work on and solve a data problem/case assigned by the lecturer and present their results to the class.
Data analysis (Part A): Presentation (5%)
Students will individually work on a data problem assigned by the lecturer. Students are to apply tools and trade statistical methods discussed during Module 1 and present their findings during Module 2 to the class.
Data analysis (Part A): Presentation - Peer assessment (5%)
Students are expected to evaluate the conduct and contributions of their fellow class mates' presentation (Data analysis (Part A): Presentation) through peer evaluation in Module 2.
Data analysis (Part A): Report/Policy brief (25%)
Taking into account feedback from the lecturer and the class the student has received for their presentation (Data analysis (Part A): Presentation), students are to submit a comprehensive policy brief/report.
Students are expected to synthesize materials, concepts, topics and tools covered throughout Modules 1 and 2. Students are expected to demonstrate their ability to apply knowledge while expressing themselves clearly and in a structured manner.
Data analysis (Part B): Presentation (10%)
Students will individually work on a data problem assigned by the lecturer. Students are to apply tools and trade statistical methods discussed during Module 1 and 2 and present their findings during Module 3 to the class.
Data analysis (Part B): Presentation - Peer assessment (5%)
Students are expected to evaluate the conduct and contributions of their fellow class mates' presentation (Data analysis (Part B): Presentation) through peer evaluation in Module 3.
Data analysis (Part B): Report/Policy brief (40%)
Taking into account feedback from the lecturer and the class the student has received for their presentation (Data analysis (Part B): Presentation), students are to submit a comprehensive policy brief/report.
Students are expected to synthesize materials, concepts, topics and tools covered throughout the course. Students are expected to demonstrate their ability to apply knowledge while expressing themselves clearly and in a structured manner
SubmissionAssignments must be submitted in:
1. Softcopy through Turnitin on MyUni
All assignments must be presented professionally with clear headings, appropriate referencing and using one and a half spacing.
Extensions will only be granted if requests are received in writing to the course coordinator at least 24 hours before the final due date unless they are requested on medical or compassionate grounds and are supported by appropriate documents. Late assignments will be penalised.
Your assignment must include the IIT assignment cover sheet which can be downloaded from MyUni under “Assignments”. Each page must be numbered with your student ID and name.
Please contact the course coordinator, preferably by email, for assistance or guidance in relation to course work, assignments or any concerns that may arise. Assignments will normally be returned two weeks after they have been submitted.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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