PP419 Half Unit
Advanced Empirical Methods for Policy Analysis
This information is for the 2022/23 session.
Teacher responsible
Professor Daniel Sturm
Availability
This course is available on the Double Master of Public Administration (ÐÓ°ÉÂÛ̳-Columbia), Double Master of Public Administration (ÐÓ°ÉÂÛ̳-Sciences Po), MPA Dual Degree (ÐÓ°ÉÂÛ̳ and Columbia), MPA Dual Degree (ÐÓ°ÉÂÛ̳ and Hertie), MPA Dual Degree (ÐÓ°ÉÂÛ̳ and NUS), MPA Dual Degree (ÐÓ°ÉÂÛ̳ and Sciences Po), MPA Dual Degree (ÐÓ°ÉÂÛ̳ and Tokyo), Master of Public Administration and Master of Public Policy. This course is available with permission as an outside option to students on other programmes where regulations permit.
Pre-requisites
The expectation is that MPA students will have previously taken PP440 and PP455 or other equivalent courses. The expectation for other students (including MPA Dual Degree students spending Year 2 at ÐÓ°ÉÂÛ̳) is that they will have prior learning in micro and macro economics, some quantitative methods of analysis, and will require permission from the course lecturer to attend the course.
Course content
This course provides an advanced treatment of the empirical methods that are used to evaluate the effectiveness of public policies. The course builds closely on the course Quantitative Approaches and Policy Analysis (PP455) and also Micro and Macroeconomics for Public Policy (PP440). Topics covered include the problem of causality, the theory and practice of randomised experiments, difference-in-differences, synthetic controls, regression discontinuity, robust and clustered standard errors, and calibration.
Teaching
This course is delivered through a combination of classes and lectures totalling a minimum of 29 hours across Michaelmas Term.
Formative coursework
The formative coursework will comprise a graded problem set.
Indicative reading
There is no single textbook for the course and many of the key readings are journal articles. James Stock and Mark Watson "Introduction to Econometrics'' remains a useful reference particularly for the material at the beginning of the course. A very good source for background reading is Joshua Angrist and Jörn-Steffen Pischke "Mastering 'Metrics: The Path from Cause to Effect''. A full reading lis