module specification

This module serves as an entry-level course in econometrics, designed for students with backgrounds outside economics and data analytics, such as business, management, and finance. Students will gain foundational knowledge of econometric principles and methods, with a focus on applying data analysis techniques to solve real-world economic problems. The module will balance theoretical understanding with practical data handling skills, using accessible software (such as Stata or R) and focusing on real-world applications. By the end of this module, students will be prepared to apply econometric analysis within economic research, business and policy contexts, providing a foundation for more advanced quantitative studies (e.g., doctoral level studies).

The module will cover statistical methods based on the econometric literature that can be used for causal inference in economics and the social sciences more broadly, empirical analyses, focus on the effects of counterfactual policies, such as the effect of implementing a government policy change, changing a price or introducing new products. In this module, you will learn how these empirical tools can improve an understanding of economic policy and decision-making using data and theory. You will learn how to pose a testable question, how to retrieve data, how to handle the data with a software, and most importantly, how to interpret the quantitative results and apply in the real world.

The module operates for 12 weeks during spring term. Module is organized into weekly lectures (1 hour) and seminars at IT lab (2 hours).

The following is a guide of each week’s topics:

Week 1: Introduction to Econometrics and Applications in Economic Consulting (LO1)

Week 2: Fundamentals of Regression Analysis (LO1, LO2, LO3; LO4; LO5)

Week 3: Multiple Regression and Nonlinear Relationships (LO1, LO2, LO3; LO4; LO5)

Week 4: Hypothesis Testing and Model Diagnostics (LO1, LO2, LO3; LO4; LO5)

Week 5: Time-Series Econometrics Basics and ARIMA (LO1, LO2, LO3; LO4; LO5)

Week 6: Enhancement week (LO1, LO2, LO3; LO4; LO5)

Week 7: Heteroscedasticity and Autocorrelation (LO1, LO2, LO3; LO4; LO5)

Week 8: Categorical Variables and Interaction Terms (LO1, LO2, LO3; LO4; LO5)

Week 9: Probit and Logit Models for Binary Outcomes (LO1, LO2, LO3; LO4; LO5)

Week 10: Instrumental Variables, Causality, and Difference-in-Differences (LO1, LO2, LO3; LO4; LO5)

Week 11: Revision and preparation for Coursework (LO1, LO2, LO3; LO4; LO5)

Week 12: Final revision (LO1, LO2, LO3; LO4; LO5)

Teaching is structured around a 1-hour lecture and a 2-hour seminar session per week. Lectures will be structured with the focus econometrics theoretical frameworks and present explanatory examples of real-world cases such as economic-policy and other empirical economic research.

The objective is to prepare students for independent data analysis and provide the skills to apply data analysis to conduct economic research. The teaching and learning activities in the seminar sessions will be take place in the IT lab. Students will learn to apply software to read, structure, visualise and analyse data. Software can be adapted based on IT availability – e.g., Stata, R.

Lecturer will encourage teamwork and active participation in the lectures as well the seminars. Participation in debates and speaking in class- will help students build public speaking skill and expressing their own opinion in a concise manner. Lecture notes and other resources will be made available to students on the virtual learning platform (Weblearn).

Teaching, learning, and assessment activities are designed to develop and analytical skills and equip students to apply econometric analysis in decision-making. Importantly, students will be encouraged to work independently on data collection and hypothesis testing to learn to defend their arguments in a sound manner applicable in business and academic environments. Students will build on their knowledge acquired in autumn term modules such as ‘Data analysis’ and gain in-depth skills in Econometric research.

Upon successful completion of this module, students should be able to:

1. Understand Core Econometric Concepts (LO1) Explain the purpose and scope of econometrics, including the basic principles of statistical and regression analysis as they apply to economics.

2. Construct testable economic hypothesis (LO2) Conduct basic descriptive and inferential statistical analyses and interpret the results in economic and business contexts.

3. Collect and analyse data to test hypothesis (LO3) Learn to collect appropriate dataset to test hypothesis. Familiarize themselves with econometric software methods (e.g., Stata, R) to organize, clean, and analyse datasets. Use these tools to perform regression analysis and diagnostic testing.

4. Interpret Econometric Models (LO4) Interpret and critique the results of regression models, focusing on understanding assumptions, limitations, and implications of the results.

5. Develop Analytical Skills for Decision-Making (LO5) Apply econometric results to inform business and policy decision-making, demonstrating how data can support or challenge economic hypotheses.

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