Econometric Methods for Applied Research II (API-115)
API-114 and API-115 comprise a two-course sequence for first-year doctoral students seeking training in econometric methods at a level that prepares them to conduct professional empirical research. API-115 (spring) begins by reviewing the foundations of causal inference, covering potential outcomes, the selection problem, and randomized evaluations. The course will then cover instrumental variables, regression discontinuity, bunching estimators, difference-in-differences and synthetic control methods, and machine learning. The course will emphasize a mixture of theory and application, with problem sets focused on the replication or extension of recent papers utilizing these methods.
Also offered by the Economics Department as Economics 2115 and the Business School as 4175. The two-course sequence is open only to qualified PhD students from HKS, HBS, GSE, and HSPH, but occasionally others may be admitted at the discretion of the instructor (if the instructor is convinced that such individuals can perform well and would not negatively affect the nature and pace of the course).
Advanced Quantitative Methods II: Econometric Methods (API-210)
Intended as a continuation of API-209, Advanced Quantitative Methods I, this course focuses on developing the theoretical basis and practical application of the most common tools of empirical analysis. In particular, we will study how and when empirical research can make causal claims. Foundations of empirical analysis will be coupled with hands-on examples and assignments involving the analysis of data sets. Since the course is targeted to first-year students in the MPA-ID program, we will not shy away from using mathematical tools, but the emphasis of the course will be on the conceptual understanding and application of the tools rather than on the math or the mechanics behind the tools. So, for example, when studying a given model, we will place a heavier emphasis on what the model is doing, when to use it, and how to interpret its results, as opposed to the mathematical proofs underpinning the model.