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9 - Functional Form Robustness

from Part III - Expanding the Multiverse

Published online by Cambridge University Press:  28 February 2025

Cristobal Young  and
Erin Cumberworth
By
Sheridan A. Stewart
Cristobal Young
Affiliation:
Cornell University, New York
Erin Cumberworth
Affiliation:
Cornell University, New York
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Summary

Functional form assumptions are central ingredients of a model specification. Just as there are many possible control variables, there is also an abundance of estimation commands and strategies one could invoke, including ordinary least squares (OLS), logit, matching, and many more. How much do empirical results depend on the choice of functional form? In this chapter we demonstrate the functional form multiverse with two empirical applications: how job loss affects wellbeing in panel data and the effect of education on voting for Trump. We find in our cases that OLS and logit produce very similar results, but that matching estimators can be surprisingly unstable. We also reconsider an important many-analysts study and find that human researchers produce a much wider range of results than does the multiverse algorithm.

Keywords

functional formfunctional form robustnessmatchingcoarsened exact matching (CEM)inverse probability weightinglogitlinear probability modelpanel datamarginal effects
Type
Chapter
Information
Multiverse Analysis
Computational Methods for Robust Results
 , pp. 125 - 153
Publisher: Cambridge University Press
Print publication year: 2025

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