Handbooks and reading lists in Advanced Econometrics and Quantitative Techniques

Readings, lecture slides, and problem sets from a course delivered in 2024 using the R language. "This course introduces practical tools and econometric techniques used to conduct empirical analysis on topics like equality of opportunity, education, racial disparities, and more. These skills include data acquisition, project management, version control, data visualization, efficient programming, and tools for big data analysis."

A course for PhD students addressing "modern techniques in machine learning, statistics, and computer science for estimating the uncertainty of black box forecasts. This includes conformal prediction, calibration and multi-calibration, outcome indistinguishability, and recent techniques for producing worst-case empirical coverage guarantees without any distributional assumptions." Includes a full set of lecture videos and links to readings.

Archived site for a graduate-level course that ran in Autumn 2013, with reading lists and detailed PDF notes from 26 lectures. The main text used is James D. Hamilton's "Time Series Analysis" and the top-level sections of the course are: Stationary Time Series, Multivariate Stationary Analysis, Univariate Non-Stationary Processes, Multivariate Non-Stationary, GMM and Related Issues, Likelihood Methods, and Bayesian Methods.