spur-python

spur-python implements diagnostics and correction methods for spatial unit roots developed by Müller & Watson 2024 in Python.

Citation: If you use this package, please cite Becker, Boll and Voth 2026 and Müller & Watson 2024. 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

You can install the package with:

uv pip install spur-python

Example Usage

We expose both the individual test functions and convenience wrappers running the entire pipeline.

To run the full pipeline, use the spur() wrapper:

import spur
from spur import load_chetty_data, standardize

#  --- data processing ---
df = load_chetty_data()

df = df[~df.state.isin(["AK", "HI"])][["am", "fracblack", "lat", "lon"]]
df = df.dropna(subset=["am", "fracblack", "lat", "lon"])
df = standardize(df, ["am", "fracblack"])

# --- spur pipeline ---
result = spur.spur(
    "am ~ fracblack",
    df,
    lon="lon",
    lat="lat",
    q=10,
    nrep=500,
    seed=42,
)
print(result.summary())

This prints both the spur-diagnostics:

--------------------------------------------
--------------------------------------------
              SPUR Diagnostics              
--------------------------------------------
Test              LR             p-value    
i0                     4.2961         0.0080
i1                     2.5240         0.4660
i0resid                3.3153         0.0700
i1resid              570.7543         0.2540
--------------------------------------------
--------------------------------------------

and the regression results with transformed and untransformed variables:

             Regression results             
--------------------------------------------
--------------------------------------------
                              am            
                  --------------------------
Coefficient       Levels         Transformed
--------------------------------------------
Intercept             -0.0000        -0.0000
                     (0.1732)       (0.0789)
fracblack             -0.6009        -0.4240
                     (0.1187)       (0.0903)
--------------------------------------------
N                         693            693
R-squared              0.3611         0.1029
Adj. R-squared         0.3601         0.1016
SCPC q                      8              8
SCPC cv                2.6097         2.6097
SCPC avc               0.0300         0.0300
--------------------------------------------

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 26(2): 177–202. https://doi.org/10.1177/1536867X261449932.

Chetty, Raj, Nathaniel Hendren, Patrick Kline, Emmanuel Saez "Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States" , The Quarterly Journal of Economics 129(4) (2014), 1553–1623, https://doi.org/10.1093/qje/qju022

Müller, Ulrich K. and Mark W. Watson "Spatial Unit Roots and Spurious Regression", Econometrica 92(5) (2024), 1661–1695. doi:10.3982/ECTA21654.