What is not considered a drawback of using linear probability models for Bernoulli responses?

The correct answer indicates that all the listed factors are considered drawbacks of using linear probability models (LPM) for Bernoulli responses.

Heteroscedasticity is a significant concern in linear probability models because the variance of the error terms can change depending on the predicted probabilities. As the probabilities approach the extremes of 0 or 1, the model can yield non-constant variance, violating one of the core assumptions of ordinary least squares (OLS) regression. This can lead to inefficient estimates and unreliable hypothesis tests.

Multicollinearity arises when explanatory variables are highly correlated, which can also pose challenges in linear probability models. This affects model interpretation and may inflate the standard errors of the coefficients, leading to less reliable significance tests.

Additionally, the issue of fitted values being unreasonable stems from the nature of LPM, where predicted probabilities can fall outside the valid range of [0, 1]. This violation is particularly problematic since it contradicts the basic properties of probability.

Considering these challenges, it's clear that all the mentioned issues are indeed drawbacks associated with using linear probability models for Bernoulli responses.