We develop tools for applied (micro-) econometrics in Python.
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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.
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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.
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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.
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wildboottest implements multiple fast wild cluster bootstrap algorithms as developed in Roodman et al (2019) and MacKinnon, Nielsen & Webb(2022).
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fastmatch implements fast matching estimators for causal inference that uses faiss for nearest neighbours.
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gmm helps you minimize generalized methods of moments (GMM) objective functions.
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jaxonometrics Library with performant implementations of standard econometrics routines in the JAX ecosystem (for econometrics on the GPU).
