Dr Michael Callen 32L.3.18

This course is compulsory on the BSc in Econometrics and Mathematical Economics and BSc in Economics. This course is not available as an outside option nor to General Course students.

Students must have completed Economics (EC1P1) and Elementary Statistical Theory I (ST109).

Students must also either have completed Quantitative Methods (Mathematics) (MA107) or else be taking Mathematical Methods (MA100) alongside.

This course is an applied introduction to econometrics. Its aim is to introduce students to the principles of estimation, statistical inference, and the central tool of regression. The course draws heavily on empirical questions and you will work with statistical software analysing actual data sets and learn some basic programming and data handling skills in the process. You will learn how statistical tools can be used to answer causal questions (e.g., “What is the causal effect of electing a better educated politician on the quality of service delivery?”). as well as prediction problems (e.g., "what individual characteristics, such as income or education, predict who political parties select to run for office?").

Topics include: program evaluation; randomised experiments; univariate regression; omitted variable bias; selection bias; sampling fluctuation; statistical inference; standard errors; and hypothesis testing.

20 hours of lectures and 10 hours of lectures in the LT. 1 hour of lectures in the ST.

There will be a reading week in Week 6 of LT only (no lectures or classes that week).

This course is delivered through a combination of classes and lectures totalling a minimum of 30 hours across Lent Term.  

There are weekly assignments and feedback will be given on two.

• J. D. Angrist and J. S. Pischke Mastering ‘Metrics. The Path from Cause to Effect, Princeton University Press.
• J. H. Stock and M. Watson Introduction to Econometrics, Pearson

Exam (85%, duration: 2 hours, reading time: 15 minutes) in the summer exam period.
Coursework (15%) in the LT.