econometricsmodels
A Python API providing statistical and econometric analysis methods. It is primarily intended for use as the analysis engine for economicon, a GUI application for data analysis, and its design prioritizes ease of embedding from scripts and programs (type completion, validation, dynamic construction).
- The computational core is implemented in Rust and thinly bound to Python via PyO3.
- Data input is restricted to polars DataFrames only, passed to the Rust side via Arrow zero-copy.
- Formula-string parsing (e.g.
y ~ x1 + x2) is not used. The dependent variable is passed as a single column name (str), independent variables as a list of column names (list[str]), and estimation options as an instance of a dedicated class.
Installation
pip install econometricsmodels
Requires Python 3.12 or later.
Quickstart
OLS
import polars as pl
from econometricsmodels import OLS
df = pl.DataFrame(
{
"y": [2.1, 3.9, 6.2, 8.1, 9.8],
"x1": [1.0, 2.0, 3.0, 4.0, 5.0],
}
)
result = OLS(df, y="y", x=["x1"]).fit()
print(result.params) # {"const": ..., "x1": ...}
print(result.std_errors) # {"const": ..., "x1": ...}
print(result.r_squared)
# Row-oriented parameter table (param/coef/std_err/t_stat/p_value/conf_lower/conf_upper).
print(result.coef_table())
# Overall-fit statistics.
print(result.f_statistic, result.f_p_value)
print(result.aic, result.bic)
Logit
from econometricsmodels import Logit
df = pl.DataFrame(
{
"y": [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0],
"x1": [1.0, 1.5, 2.0, 2.5, 3.0, 3.2, 3.8, 4.0, 4.5, 5.0],
}
)
result = Logit(df, y="y", x=["x1"]).fit()
print(result.coef_table()) # same shape as OLS, but z_stat instead of t_stat
print(result.aic, result.bic)
# Likelihood-ratio test for overall significance (the Logit/Probit analogue
# of OLS's F-statistic), plus McFadden pseudo R-squared.
print(result.lr_statistic, result.lr_p_value)
print(result.pseudo_r_squared)
Probit has the same API shape as Logit (drop-in replacement).
For more details — including how to switch to heteroskedasticity-robust standard errors (HC0-HC3), cluster-robust standard errors, HAC (Newey-West) standard errors, and Logit/Probit-specific features (marginal effects, classification tables) — see the documentation site. The full documentation, including the API reference, is published at https://masahiroyecon1997dev.github.io/econometricsmodels/.
Implementation status
Implemented: OLS (Ordinary Least Squares), WLS (Weighted Least Squares), Logit, Probit.
In progress: IV (2SLS/GMM).
Planned next, in this order: Tobit → FE (Fixed Effects) → RE (Random Effects) → GLS.
During the 0.x.x pre-release period, breaking changes may occur even in minor version bumps.
Verification accuracy
Estimates for each implemented method (coefficients, standard errors, confidence intervals, AIC, BIC, log-likelihood, and the model's overall-significance test — F-statistic/p-value for OLS/WLS, likelihood-ratio statistic/p-value for Logit/Probit — plus R²/adjusted R² for OLS/WLS and McFadden pseudo R² for Logit/Probit) are verified by numerical comparison against reference implementations. In addition to a primary reference, an independent implementation is used as a cross-check.
| Method | cov_type |
Primary reference | Independent cross-check |
|---|---|---|---|
| OLS / WLS | classical / HC0-3 / cluster | statsmodels (relative tolerance 1e-8) | R (lm/lm(weights=) + sandwich/lmtest, relative tolerance 1e-8) |
| OLS / WLS | HAC (Newey-West) | statsmodels (relative tolerance 1e-8) | R (relative tolerance 1e-2 for OLS, 5e-2 for WLS — looser due to differing small-sample correction conventions) |
| Logit / Probit | classical / OPG / HC0 / cluster | statsmodels (relative tolerance 1e-8) | R (glm + sandwich/marginaleffects, relative tolerance ~2e-4 — looser due to differing optimizer convergence) |
| Logit / Probit | HC1 | R (glm + sandwich, relative tolerance ~2e-4) — used as the primary reference here, since statsmodels' discrete-choice models omit the n/(n-k) small-sample correction for HC1 |
— |
License
1 maintainer
Metadata
1 maintainer
Release files for econometricsmodels 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distributions (wheels)
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