scpc-python

scpc-python provides spatial correlation-robust inference for regression coefficients following Müller & Watson 2022 and Müller & Watson 2023, implemented in Python based on their original Stata implementation.

Citation: If you use this package, please cite Becker, Boll and Voth 2026, Müller & Watson 2022, and Müller & Watson 2023. See CITATION.bib for the BibTeX entries.

If you encounter any issues or have any questions, please open an issue on GitHub or contact the authors.

Installation

The package can be installed as standalone package:

uv pip install scpc-python

or as scpc-python dependency with:

uv pip install spur-python

Basic Usage

This example starts from the transformed branch of the Becker, Boll and Voth 2026 workflow. In the full workflow, you would first use spurtest to decide whether to stay in levels or transform. Here we assume the transformed branch and move directly to the regression plus scpc step.

# assumes spur-python is installed
from spur import load_chetty_data, spurtransform
from scpc import scpc
import statsmodels.formula.api as smf

data = load_chetty_data()

data = data[~data["state"].isin(["AK", "HI"])][
    ["am", "gini", "fracblack", "lat", "lon"]
]
data = data.dropna(subset=["am", "gini", "fracblack", "lat", "lon"]).copy()

transformed = spurtransform(
    "am ~ gini + fracblack",
    data,
    lon="lon",
    lat="lat",
    transformation="lbmgls",
    prefix="h_",
)

fit = smf.ols("h_am ~ h_gini + h_fracblack", data=transformed).fit()

result = scpc(
    fit,
    data=transformed,
    lon="lon",
    lat="lat",
    cvs=True,
)

print(result)

scpc() returns an SCPCResult object:

• print(result): prints an R-like SCPC inference table
• result.scpcstats: the main inference table with coefficient estimates, standard errors, t statistics, p values, and 95% interval endpoints
• result.scpccvs: optional stored critical values at 32%, 10%, 5%, and 1%
• result.coef(): returns named coefficient estimates in scpc-python>=0.1.2
• result.confint(): returns named confidence intervals in scpc-python>=0.1.2
• result.summary(): prints the main table plus confidence intervals in scpc-python>=0.1.2
• result.avc: the average pairwise correlation bound used in the analysis
• result.c0: the kernel scale implied by avc
• result.cv: the unconditional 5% critical value
• result.q: the number of retained non-constant spatial principal components

Key Arguments

The most important scpc() arguments in the workflow above are:

• model: the fitted regression model
• data: the data frame
• lon, lat: the geodesic coordinate column names
• coords_euclidean: use this instead of lon / lat when coordinates are Euclidean rather than geographic
• cluster: optional clustering column
• ncoef: how many coefficients to report
• avc: upper bound on the average pairwise correlation
• uncond: whether to skip the conditional adjustment
• cvs: whether to store the extra critical values

Documentation

Please refer to the package documentation for detailed information and other (R, Python, Stata) packages.

References

Becker, Sascha O., P. David Boll and Hans-Joachim Voth "Testing and Correcting for Spatial Unit Roots in Regression Analysis", The Stata Journal, forthcoming. https://pauldavidboll.com/SPUR_Stata_Journal_website.pdf.

Müller, Ulrich K. and Mark W. Watson "Spatial Correlation Robust Inference", Econometrica 90(6) (2022), 2901–2935. https://www.princeton.edu/~umueller/SHAR.pdf.

Müller, Ulrich K. and Mark W. Watson "Spatial Correlation Robust Inference in Linear Regression and Panel Models", Journal of Business & Economic Statistics 41(4) (2023), 1050–1064. https://www.princeton.edu/~umueller/SpatialRegression.pdf.