Unit information: Applied Economics in 2019/20

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. Bergemann
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

Description including Unit Aims

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.

Intended Learning Outcomes

By the end of this course, students will be able to:

1. Organise data in computer readable form ready for econometric analysis using various software packages;
2. Describe the important features of published examples of econometric analysis and evaluate strengths and weaknesses in such work;
3. Conduct econometric analyses of data using techniques taught in the accompanying Econometrics unit;
4. Conduct basic statistical programming using the software packages Stata and R.

Teaching Information

Stata part: 10 hours of lectures, 1 one-hour exercise lectures, 4 hours of small-group classes

R part: 6 hours of lecture, 1 one-hour exercise lectures, and 2 hours of small-group classes.

Assessment Information

Summative Assessment:

Individual applied economic project (maximum 10 pages). The project involves the following components:

1. students will be asked to access, merge and transform data.
2. they will be expected to read specified papers and replicate them.
3. they will then use their data to conduct their own empirical analysis that will link into the replication in (2).
4. they will be asked to solve an individual programming exercise.

Each of the components is intended to test the corresponding ILO described above.

Formative assessment will be done based on exercise sheets and programming assignments.

Reading and References

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.