Evaluating efficiency gains in the Linear Probability Model
Date
2025-09
Authors
Pacheco, Tomás Daniel
relationships.isContributorOfPublication
Sosa Escudero, Walter
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de San Andrés. Departamento de Economía
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.
Description
Fil: Pacheco, Tomás Daniel. Universidad de San Andrés. Departamento de Economía; Argentina.