Undergraduate Programme and Module Handbook 2022-2023 (archived)
Module ECON2061: ECONOMIC DATA ANALYSIS
Department: Economics
ECON2061: ECONOMIC DATA ANALYSIS
| Type | Open | Level | 2 | Credits | 20 | Availability | Available in 2022/23 | Module Cap | Location | Durham |
|---|
Prerequisites
- Principles of Economics (ECON1011) AND EITHER Economic Methods (ECON1021) OR Calculus I (MATH1061) AND Linear Algebra I (MATH1071) AND Probability I (MATH1597) AND Statistics I (MATH1617)
Corequisites
- Any Level 2 Economics module.
Excluded Combination of Modules
Aims
- To enable students to read and understand the typical empirical analysis as utilised in much of the economic literature.
- To enable students to write a report based on econometric analysis.
- To build on the material of Economic Methods
- To provide relevant material to be utilised in other core and optional modules.
Content
- Linear Regression
- Hypothesis Tests on Regression Coefficients
- Nonlinear Regression Functions
- Assessing Validity of Regression Analyses
- Regression with Panel Data
- Regression with Binary Variables
- Instrumental Variables Regression
- Introduction to Regression with Time Series Data
Learning Outcomes
Subject-specific Knowledge:
- Understand and perform regression analysis
Subject-specific Skills:
- ability to set in context results from empirical research
- ability to conduct and manage a small scale empirical project and tie the results to relevant literature
Key Skills:
- Written Communication: the summative assessment includes both a written report and a written examination.
- Problem Solving: the exercises will require students to use the basic material to solve problems tested in the summative assessment
- Initiative: students, whilst undertaking the assignment, must carry out resource investigation by accessing a range of hard copy and electronic resources, establishing the relevance of the documents for the problem in hand.
- Numeracy: students are expected to perform econometric tests and interpret empirical work to the level of their knowledge.
- Computer Literacy: the project will be word-processed and the analysis requested will require the use of an econometric package.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Teaching is by lectures, seminars and computer practicals. Learning takes place through attendance at lectures, preparation for and participation in seminar classes, computer practicals, and private study. Formative assessment is continuous in the form of quizzes. Summative assessment is by means of a written examination and a written assignment.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 20 | 1 per week | 2 hours | 40 | |
| Revision Lectures | 2 | 1 per week in Term 3 | 2 hours | 4 | |
| Seminars | 6 | 3 in Term 1, 3 in Term 2 | 1 hour | 6 | ■ |
| Computer Practicals | 2 | 1 in Term 1, 1 in Term 2 | 1 hour | 2 | ■ |
| Preparation and Reading | 148 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 80% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| One written examination | 2 hours | 100% | same |
| Component: Assignment | Component Weighting: 20% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| One written assignment | 1500 words max | 100% | same |
Formative Assessment:
One test and continuous assessment in the form of quizzes.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University