Unit name | Applied Economics |
---|---|
Unit code | ECONM1008 |
Credit points | 15 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Barsbai |
Open unit status | Not open |
Pre-requisites |
None |
Co-requisites |
ECONM1010 Microeconomics, ECONM1011 Macroeconomics, ECONM1022 Econometrics |
School/department | School of Economics |
Faculty | Faculty of Social Sciences and Law |
The aim of this unit is to enable the students to quantitatively address economic questions in a rapidly changing environment using econometric tools. This requires students to become independent of software infrastructures and predefined data structures.
To achieve this aim, the unit will be taught in two closely linked parts: One part taught using the software Stata, and one part that is taught using R. The Stata part focuses on the process of developing a research question, formulating and testing a hypothesis, and interpreting results from the econometric analyses. The R part focuses on programming skills, such as data structures, data processing, conditional statements, and optimisation. While these two parts differ in their focus, they have a common denominator in terms of linked economic applications. This unit will consist of lectures in basic statistical programming and applied econometrics drawing on applications from both microeconomics and macroeconomics. The unit will emphasise the practical issues arising from obtaining, processing and analysing data. An important component of this will be the continuous application, throughout the unit, of statistical tools being taught in the co-requisite unit, Econometrics.
By the end of this course, students will be able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities
100% Coursework
Angrist, J. D. & J.-S. Pischke (2014), “Mastering 'Metrics: The Path from Cause to Effect" Princeton University Press.
Angrist, J. D. & J.-S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Press.
Baum, C. (2009), An Introduction to Modern Econometrics Using Stata, Stata Press.
Wickham, H. & G. Grolemund (latest edition), R for Data Science.