We develop tools for applied (micro-) econometrics in Python.
pyfixest implements fast routines for fixed effects regression (OLS, IV, Poisson) and a wide range of inference procedures (iid, HC1-3, CRV1, CRV3, randomization inference, multiple testing corrections via Bonferroni & Romano-Wolf) following the syntax of the formidable fixest R package.
duckreg provides a simple interface to run lossless regressions on very large datasets that do not fit in memory by first reducing the data to a set of summary statistics out-of-memory using duckdb and then running weighted least squares with frequency weights.
maketables produces publication-ready regression tables for models from
pyfixest,statsmodels, andlinearmodelsin html (viagreat-tables), Latex and Word.within provides high-performance solvers for projecting out high-dimensional fixed effects from regression problems. It includes a Rust core and Python bindings, and exploits the graph structure of fixed-effects systems.
wildboottest implements multiple fast wild cluster bootstrap algorithms as developed in Roodman et al (2019) and MacKinnon, Nielsen & Webb(2022).
fastmatch implements fast matching estimators for causal inference that uses faiss for nearest neighbours.
gmm helps you minimize generalized methods of moments (GMM) objective functions.
jaxonometrics Library with performant implementations of standard econometrics routines in the JAX ecosystem (for econometrics on the GPU).