Evaluating efficiency gains in the Linear Probability Model

Tomás Pacheco

No 18, Young Researchers Working Papers from Universidad de San Andres, Departamento de Economia

Abstract: This paper evaluates the efficiency gains of the Adaptive Least Squares (ALS) estimator proposed by Romano and Wolf (2017) in the context of Linear Probability Models (LPM), where heteroskedasticity is inherent to the model. Using empirical applications and Monte Carlo simulations, we compare ALS to OLS and Probit estimators under three strategies for handling predicted probabilities outside the (0, 1) interval: bounding, sigmoid transformation, and trimming. The results show that efficiency gains from ALS are not systematic and depend on the correction method, with the bounding approach yielding the most substantial improvements.

Keywords: efficiency; linear probability model; weighted least squares (search for similar items in EconPapers)
JEL-codes: C01 C12 C50 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2025-09, Revised 2025-09
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Persistent link: https://EconPapers.repec.org/RePEc:sad:ypaper:18

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