Dr Michael Gmeiner (32L.4.28)

This course is compulsory on the BSc in Economic History with Economics, BSc in Economics and Economic History, BSc in Finance, BSc in International Social and Public Policy and Economics, BSc in Philosophy and Economics and BSc in Politics and Economics. This course is available on the BSc in Accounting and Finance, BSc in Data Science, BSc in Environment and Development, BSc in Environmental Policy with Economics, BSc in Geography with Economics, BSc in Mathematics and Economics, BSc in Mathematics with Economics, BSc in Mathematics, Statistics and Business, BSc in Philosophy, Politics and Economics and Diploma in Accounting and Finance. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.

Note, EC2C3 is mutually exclusive with EC220, EC221 and MG205.

Students will have completed Elementary Statistical Theory (ST102) or Quantitative Methods (Statistics) (ST107), or equivalent.

This course is an applied introduction to econometrics. The focus is on regression-based techniques and interpreting results in applied settings. The course will centre on how statistical tools can be used to answer causal “what-if” questions (e.g., “What is the effect of years of education on income?”). You will work with statistical software to analyse actual data sets and will learn basic programming in Stata through dedicated workshops. Topics include: randomised experiments, programme evaluation, matching, simple and multiple regression analysis, inference, omitted variable bias, functional form specification, measurement error, missing data, reverse causality, and instrumental variables.

30 hours of lectures, 10 hours of classes, and 5 online Stata workshops in the MT.

Student learning will be supported through the EC2C3 Support Lab and through a dedicated discussion forum.

Students are expected to engage with the problem sets each week. At least two of these will be marked and feedback provided.

Lecture materials are complemented by reading of J. D. Angrist and J. S. Pischke, Mastering ‘Metrics. The Path from Cause to Effect, Princeton University Press.

Lecture materials are self-contained with regards to econometric theory, so reading of econometrics textbooks is not required. The following texts are recommended for students interested in consulting a textbook.

• J. Wooldridge, Introductory Econometrics. A Modern Approach, Cengage

• J. H. Stock and M. Watson, Introduction to Econometrics, Pearson

Exam (100%, duration: 2 hours, reading time: 15 minutes) in the January exam period.