econometricsmodels
統計・計量経済学の分析手法を提供するPython APIです。分析GUIアプリ「economicon」のエンジンとして使われることを主な用途としており、スクリプト・プログラムからの組み込みやすさ（型補完・バリデーション・動的な組み立て）を優先した設計になっています。
- 計算コアは Rust で実装し、PyO3 で薄くPythonにバインディングしています。
- データ入力は polars DataFrameのみに限定し、ArrowのゼロコピーでRust側に受け渡します。
y ~ x1 + x2のようなformula文字列パースは採用せず、被説明変数は列名（str）、説明変数は列名のリスト（list[str]）、推定オプションは専用クラスのインスタンスとして渡します。
インストール
pip install econometricsmodels
対応Pythonバージョンは3.12以上です。
クイックスタート
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)
不均一分散に頑健な標準誤差（HC0〜HC3）・クラスター標準誤差・HAC（Newey-West）標準誤差への切り替えなど、詳しい使い方はdocs/getting-started.mdを参照してください。
検証精度
各手法の推定値（係数・標準誤差・信頼区間・R²・調整済みR²・AIC・BIC・対数尤度・F統計量・F検定p値）は、リファレンス実装との数値比較で検証しています。主リファレンスに加え、独立実装によるクロスチェックも行っています。
| 手法 | cov_type | 主リファレンス | 独立クロスチェック |
|---|---|---|---|
| OLS | classical / HC0-3 / cluster | statsmodels（相対誤差1e-8） | R（lm + sandwich/lmtest、相対誤差1e-8） |
| OLS | HAC（Newey-West） | statsmodels（相対誤差1e-8） | R（相対誤差1e-2。小標本補正の慣習差のため緩め） |
| WLS | classical / HC0-3 / cluster | statsmodels（相対誤差1e-8） | R（lm(weights=) + sandwich/lmtest、相対誤差1e-8） |
| WLS | HAC（Newey-West） | statsmodels（相対誤差1e-8） | R（相対誤差5e-2。小標本補正の慣習差のため緩め） |
実装状況
現在実装済みなのは Phase 1（基礎回帰）のうち OLS（最小二乗法）・WLS（加重最小二乗法） です。区分回帰を含む他の手法は未着手です。
0.x.xのプレリリース期間中は、マイナーバージョンの変更でも破壊的変更が入る可能性があります。
ライセンス
1 maintainer
Metadata
1 maintainer
Release files for econometricsmodels 0.2.0
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