Critically appraise concepts and assumptions underlying the econometric and time-series methods considered in the module.

Knowledge and Understanding, Learning


Demonstrate an understanding of standard econometric approaches to testing economic theories using appropriate data

Knowledge and Understanding, Application

Critically evaluate various alternative econometric and time-series methods

Analysis, Problem Solving


Critically evaluate statistical findings related to economics and finance

Analysis, Reflection, Communication

(1) MIDTERM ASSIGNMENT - INDIVIDUAL REPORT (1000 WORDS) weighted at 30% - LOs 1,2

(2) FINAL ASSIGNMENT - INDIVIDUAL REPORT (2,000 WORDS) weighted at 70% - LOs 1, 2, 3, & 4.

The module will introduce students to knowledge of econometric methods. They will also be able to develop a critical appreciation of the uses and shortcomings of various econometric methods and techniques whereby they will be introduced to certain problems involved in modelling and forecasting with time-series data. This module will also enhance students' statistical and analytical skills to the point where they are able to approach the analysis and interpretation of economic data with confidence and experience and they will also be able to explore a wide range of topical applications of econometrics.
There are numerous econometric problems in the data available for empirical testing. This module concentrates on the introduction of econometric tools to analyze empirically, ways of identifying and dealing with these problems whereby they will be exposed to diagnostic tests and criteria for choosing models. Topics include:
• The Nature of Econometrics and Economic Data.
• The Simple Regression Model.
• Multiple Regression Analysis: Estimation, Inference, OLS Asymptotics, and Further Issues.
• Multiple Regression Analysis with Qualitative Information.
• Econometric problems: Model misspecification, serial correlation, and heteroscedasticity.
• Basic Regression Analysis with Time Series Data.
• Serial Correlation and Heteroskedasticity in Time Series Regressions.

The learning strategy for the module requires students to commit 200 learning hours (including assessment) of which there will be 36 hours of tutor-led learning and 164 hours of independent and self-directed study. During the tutor-led learning hours, students will receive robust support through interactive tutorials where they can ask questions, clarify doubts, and receive personalized guidance on challenging concepts. These tutorials are designed to foster a deeper understanding of the material, with opportunities for collaborative learning and discussions that enhance comprehension and retention.

Action Learning. Learning is achieved by engaging students in activities that have elements of problem solving combined with intentional learning.
Authentic Learning. Students will be presented with activities that are framed around "real life" contexts in which students will find learning more meaningful and motivating. Thus, they will be more engaged in the process of acquiring knowledge.

You will undertake ‘formative’ assessments during the module to help you monitor your learning and provide you and us with ongoing feedback on your progress, that helps you prepare for the ‘summative assessment(s) during or at the end of the module. The formative assessments may include quizzes, short essays, or problem-solving exercises, with feedback given to help students identify areas for improvement.

The use of AI tools such as ChatGPT can be beneficial in learning, however, students must adhere to the following guidelines: (i) all submitted work must be original and written by the student; (ii) if a student utilizes AI tools to generate ideas or receive guidance, this must be clearly cited in the submitted work, just as any other source would be; (iii) students should be mindful of the ethical implications of using AI in their studies, ensuring that their use of such tools does not constitute academic dishonesty.

Unit feedback:
During semester, students’ feedback on the module will be collated and distributed through University's online platform, ensuring consistency with other units and focusing on both improvement and future success. This process includes both individual feedback and cohort-wide summaries, aligning with the university's standardized approach to learning and assessment.

Main Texts:
1. Wooldridge, Jeffrey M., (2024). Introductory Econometrics: A Modern Approach, 8th Edition. Cengage.
2. Dougherty, C. (2016). Introduction to Econometrics, 5th ed. Oxford University Press

Additional Texts:
1. Studenmund, A.H. (2021) Using Econometrics: A Practical Guide, 7th ed. Pearson.
2. Dougherty, C. (2011) Introduction to Econometrics, 4th Edition. Oxford University Press.

Module Study Guide and Handbook
BUV Learning Resources Centre and website
BUV Canvas; Learning Management System
SU VLE
SU Library