Work from two doctoral econometrics sequences — the Stockholm University Ph.D. programme (Econometrics I–II, 2024) and the Yale Ph.D. sequence (Econometrics III–IV, 2025–26).
The Yale write-ups restate each problem before solving it; the Stockholm ones answer without
restating, so each is paired with the problem set it answers (*_problems.pdf). Everything else
here is my own work. No lecture slides, official or suggested solutions, syllabi or textbooks
are hosted here.
Six write-ups, 26 pp. Probability and statistics; least-squares algebra; conditional expectations and the law of iterated expectations; asymptotic theory and inference; variance estimation under general error structures; GMM and constrained minimum distance. Taught from Bruce Hansen, Probability and Statistics for Economists and Econometrics.
Five write-ups, 21 pp. Potential outcomes and identification of the ATT; randomization inference and assignment probabilities; simulation and instrumental variables; SUTVA under spillovers; difference-in-differences with staggered adoption. Taught from Angrist and Pischke, Mostly Harmless Econometrics, and Imbens and Rubin Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction.
yale/econometrics-iii/ — ECON 5552, Fall 2025 (Prof. Yuichi Kitamura)
Five write-ups, 31 pp, completed at full workload while auditing. Parametric and nonparametric identification; asymptotic theory for nonlinear models; limited-dependent-variable models — probit, censored and Poisson regression; quantile instrumental variables; Kolmogorov–Smirnov and related distributional tests; the bootstrap and subsampling, applied to Survey of Consumer Finances data. The course runs from identification through asymptotics, maximum likelihood and GMM to set identification and high-dimensional methods. No required text; suggested references included van der Vaart, Asymptotic Statistics, Wasserman, All of Nonparametric Statistics, and Wainwright, High-Dimensional Statistics.
yale/econometrics-iv/ — ECON 5553, Econometrics of Dependent Data, Spring 2026 (Prof. Timothy Christensen)
Four write-ups, 25 pp. Matching estimators and treatment effects; martingales, uniform integrability and tail sigma-algebras; pre-testing and the uniformity of inference; the Wold decomposition for stationary processes; stable convergence and Bartik instruments; limit theory for dyadic data and under two-way dependence; narrative identification. A research-level course on dependent data, running from ergodic theory and martingales through time series, panels and structural VARs to network dependence. No assigned textbook; taught from the instructor's notes. Plus two longer pieces, both course requirements:
abgrs_presentation.pdf— oral-examination presentation of Andrews, Barahona, Gentzkow, Rambachan and Shapiro, "Causal Interpretation of Structural IV Estimands" (Quarterly Journal of Economics 140(3), 2025).referee_report_bchl.pdf— referee report, written to Econometrica guidelines, on Borusyak, Chen, Hull and Lei, "Nonparametric Identification of Demand without Exogenous Product Characteristics" (NBER Working Paper 34842).
Marek Chadim · marek.chadim@yale.edu · marek-chadim.github.io