Econometric Methods and Applications (ECO139)

This course will examine cross-sectional, time-series, and panel models to facilitate a deeper understanding of their underlying principles and applications to real-world datasets. The focus of this course is less on the error-correction aspect of econometrics and more on the substance behind the models studied.  The primary objective is to equip students with the knowledge of selecting empirically sound models that actually test their hypotheses. The curriculum will emphasise Generalised Linear Models (GLMs) and Panel Models.

The course will start with a review of Ordinary Least Squares (OLS) regression, followed by GLMs, such as Logit, Probit, Negative Binomial, and Poisson regressions. Furthermore, it will revisit selected time-series models (e.g., AR(I)MA(X) and Vector Autoregression). The substantial portion of the syllabus will be devoted to Panel Methods, such as Pooled OLS, Fixed-Effects, Random Effects, Panel VARs, Panel GLMs, and Meta-Analysis. Subject to available time and student preferences, the inclusion of survival methods as an additional topic will be considered.

The technical language that we will use throughout the course is R. Familiarity with R and R Studio is not required but highly recommended.

• To understand the terminology and principles used in econometrics.
• To understand the basic concepts of data gathering, wrangling, and cleaning.
• To gain proficiency in R programming. To learn about R programming and tools that make it more efficient, such as R Studio, and R Markdown.
• To gain deep knowledge of methods used for cross-sectional data, such as OLS and GLMs.
• To learn how to model time and about forecasting using time-series data.
• Visualize data, models, and forecasts using GGPLOT.
• Learn how and when to apply complex methods, such as duration model, experimental designs and panel models.