Module Content

Throughout the module, students will use Python and/or R for statistical analysis.

Introduction to Econometrics
Nature and scope of econometrics. Economic questions and empirical analysis. Types of data: cross-sectional, time series, and panel data.
Regression Analysis of Cross-Sectional Data
Review of the Simple Linear Regression Model and Multiple Regression Analysis. OLS estimators and their properties. Goodness-of-fit metrics. Review of inference in Regression Analysis: Hypothesis testing: t-tests and F-tests and Confidence intervals.

Advanced topics: Incorporating logarithms, polyonyms, categorical variables and Interaction terms. Model misspecification and omitted variable bias.
Multicollinearity and its consequences. Heteroskedasticity and robust standard errors. Applications with cross-sectional data using Python/R.
Endogeneity and Instrumental Variables
Sources of endogeneity. Instrumental variable (IV) estimation. Two-Stage Least
Squares (2SLS) method. Applications with economic data using Python/R.
Regression analysis of Panel data
From cross-sectional and time series to panel data. Advantages of panel data. Simple Panel Data Methods. Fixed effects and random effects models.
Applications in economic research. Applications with economic panel data using Python/R.
Advanced topics
Binary choice models: Linear Probability Models, Probit, and Logit models. Tobit and Simultaneous Equation models. Applications with economic data using Python/R.

Teaching Methods

The module will be delivered on campus, with weekly lecture and tutorial sessions.

Printed notes will be given ahead of time for each section of the course, to support and enhance students’ preparation and engagement during class sessions. Lectures will follow the notes, with discussions of the main theoretical topics, and study of examples of the applications of the theory. There will be a strong emphasis on student involvement in discussions in lectures, to encourage a more active approach to learning the material, and to allow the delivery to be tailored to build on the students’ current understanding. Throughout the module, students will use Python and/or R for statistical analysis.

Regular formative work in tutorial sessions will allow students to internalise the mathematical ideas and methods developed in the lectures, and lead to the development of problem-solving skills. This formative work will also feed back into the delivery of lectures and tutorials.

Assessment Methods

The module is assessed through a Portfolio of exercises and an examination.