This course will provide a solid grounding in recent developments in applied micro-econometrics, including state-of-the art methods of applied econometric analysis. Some of these methods are related to work by recent Nobel Prize in Economics winners J. Angrist, D. Card and G. Imbens.
The course will combine both analytical and computer-based (data) material to enable students to gain practical experience in analysing a wide variety of econometric problems. It will also discuss how modern data science approaches can be used to answer important economic questions. Students will be reading various applied economic papers which apply the techniques being taught. Applications that will be considered include labour, development, industrial organisation and finance.
The topics include analysis of matching methods, identification of average, local average and marginal treatment effects using instrumental variables, regression discontinuity, randomised control experiments, post-estimation diagnostics, cross section and panel data with static and dynamic models, binary choice models and binary classification methods in machine learning, maximum likelihood estimation, ridge regression, lasso regression, and principal component regression.
Lectures are complemented with computing exercises using real data in R or Stata.
This course is ideal for advanced undergraduate students, graduate students, early-career academic researchers, and researchers in the public, private or non-profit sector.
