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Evaluating efficiency gains in the Linear Probability Model

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  • Tomás Pacheco

    (Department of Economics, Universidad de San Andrés)

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.

Suggested Citation

  • Tomás Pacheco, 2025. "Evaluating efficiency gains in the Linear Probability Model," Young Researchers Working Papers 18, Universidad de San Andres, Departamento de Economia, revised Sep 2025.
    • Handle: RePEc:sad:ypaper:18
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    File URL: https://webacademicos.udesa.edu.ar/pub/econ/ydoc18.pdf
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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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