E

econometrics

Python research code comparing interrupted time series (segmented regression) and Bayesian structural time series counterfactuals. Includes a Gibbs-sampled BSTS, Monte Carlo experiments on trends, seasonality, effect shape and triggered launches, and a placebo study of the 1983 UK seat belt law.

Python pipeline for estimating average and marginal CO2 emission factors in Portugal and Spain from ENTSO-E data, with econometric models, load-shifting experiments, and synthetic dispatch simulations.

Reproducible causal inference and econometrics in Python: simulations, quasi-experiments, experiments, causal ML and marketing mix models, with explicit identification assumptions and diagnostics.

Python research code for synthetic control, donor-pool geometry, and placebo inference. Includes DiD, ridge-augmented SC, synthetic DiD, Monte Carlo experiments, and the California Proposition 99 case study.

Python research code for sensitivity analysis in double machine learning. A twin design, in which the hidden confounder copies an observed benchmark covariate, tests a benchmark-calibrated omitted-variable-bias bound when the benchmark is itself estimated, with an application to 401(k) eligibility and a mathematical supplement.

Learn from books in statistics, econometrics, ML and DL. Follows https://trics.me/textbook/index.html, but takes a current selection of books and goes through TOC and code examples.

Delievered at https://epogrebnyak.gitlab.io/statbooks/

📈 Estimation of the determinants for the Body Mass Index and determinants of being obese at all.