Evaluating efficiency gains in the Linear Probability Model

dc.contributor.Mentor	Sosa Escudero, Walter
dc.creator.Autor	Pacheco, Tomás Daniel
dc.date.accessioned	2025-10-13T17:45:27Z
dc.date.available	2025-10-13T17:45:27Z
dc.date.issued	2025-09
dc.description	Fil: Pacheco, Tomás Daniel. Universidad de San Andrés. Departamento de Economía; Argentina.
dc.description.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.
dc.format	application/pdf
dc.identifier.uri	https://repositorio.udesa.edu.ar/handle/10908/25824
dc.language	eng
dc.publisher	Universidad de San Andrés. Departamento de Economía
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title	Evaluating efficiency gains in the Linear Probability Model
dc.type	Tesis
dc.type	info:eu-repo/semantics/masterThesis
dc.type	info:ar-repo/semantics/tesis de maestría
dc.type	info:eu-repo/semantics/updatedVersion