杏吧论坛

Dr Casey Kearney

This course is available on the Double Master of Public Administration (杏吧论坛-Columbia), Double Master of Public Administration (杏吧论坛-Sciences Po), Double Master of Public Administration (杏吧论坛-University of Toronto), MPA Dual Degree (杏吧论坛 and Hertie), MPA Dual Degree (杏吧论坛 and NUS), MPA Dual Degree (杏吧论坛 and Tokyo), MPA in Data Science for Public Policy, Master of Public Administration and Master of Public Policy. This course is not available as an outside option.

Students must have completed Quantitative Approaches and Policy Analysis (PP455).

Requires successful completion or exemption from PP455 or an equivalent course.

Basic knowledge in R or an equivalent programming language is required. Students who do not have prior knowledge of R will be required to take an R module with the Digital Skills Lab.

This course provides a hands-on introduction to model-based inference strategies including prediction, forecasting models and applications of simulation analysis and Bayesian models. The course begins by distinguishing forecasting and prediction accuracy as distinct goals in statistical learning and the core ideas of training / testing splits, the bias-variance trade-off and measuring model accuracy. Initial weeks of the course will then focus on time series applications and survival models and censored data. Later lectures will then introduce simulation analysis as a tool and its applications in Bayesian data analysis and hierarchical models. Final weeks will cover MCMC techniques and their application to both time series switching models and Bayesian analysis. Basic models in text analysis will also be discussed if time permits. Lessons will include analysis of data sets from healthcare, education, and international finance applications.

20 hours of lectures and 10 hours of seminars in the WT.

Students will be expected to produce 1 problem sets in the WT.

The formative coursework will comprise a graded problem set.

• Gelman, Andrew, Jennifer Hill, and Aki Vehtari. Regression and other stories. Cambridge University Press, 2021.
• Gelman, Andrew, et al. Bayesian data analysis. Chapman and Hall/CRC, 1995.
• Kruschke, John. "Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan." (2014).
• Cryer, Jonathan D., Kung-Sik Chan, and Kung-Sik.. Chan. Time series analysis: with applications in R. Vol. 2. New York: Springer, 2008.
• James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.
• Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2018.

Exam (70%, duration: 2 hours) in the spring exam period.
Project (30%, 2000 words) in the WT.