optimalgiv 0.1.9

Python ⇄ Julia bridge for the OptimalGIV package

optimalgiv

A minimal Python wrapper for OptimalGIV.jl

This interface enables Python users to call Granular Instrumental Variables (GIV) estimators directly on pandas DataFrames using JuliaCall. Julia is automatically installed and all dependencies are resolved without manual setup.

---

Installation

pip install optimalgiv

On first use, optimalgiv will automatically:

• Install Julia (if not already available)
• Install OptimalGIV.jl and supporting packages
• Precompile and create a self-contained Julia environment

---

Quickstart

import pandas as pd
import numpy as np
from optimalgiv import giv

df = pd.DataFrame({
    "id":  np.repeat([1, 2], 5),
    "t":   list(range(1, 6)) * 2,
    "q":   np.random.randn(10),
    "p":   np.random.randn(10),
    "η1":  np.random.randn(10),
    "η2":  np.random.randn(10),
    "absS": np.abs(np.random.randn(10)),
})

model = giv(
    df,
    "q + endog(p) ~ id & (η1 + η2)",
    id="id", t="t", weight="absS",
    algorithm="scalar_search",
    guess={"Aggregate": 2.0}
)

print(model.coef)
print(model.coefficient_table())

---

References

• Gabaix, Xavier, and Ralph S.J. Koijen.
  Granular Instrumental Variables.
  Journal of Political Economy, 132(7), 2024, pp. 2274–2303.

• Chaudhary, Manav, Zhiyu Fu, and Haonan Zhou.
  Anatomy of the Treasury Market: Who Moves Yields?
  Available at SSRN: https://ssrn.com/abstract=5021055