What are the advantages and disadvantages of the probit model?

The primary advantage of the methodology lies in its simplicity. Estimating and interpreting the inferences are straightforward, mirroring the process in multiple regression analysis. A key aspect to note is the necessity for utilizing heteroskedasticity-robust standard errors to ensure accurate and reliable results.

However, a significant disadvantage arises when considering the assumption of linearity in the probability function. It raises the question of whether it is logical for the probability of an event to be linearly dependent on the predictor variable X. This linearity assumption may not always hold true in real-world scenarios.

Furthermore, the predicted probabilities derived from this methodology can fall outside the valid range of 0 to 1. Specifically, the probabilities could be less than 0 or greater than 1, which contradicts the fundamental property of probabilities. This limitation undermines the practical applicability and interpretability of the results.

Ideally, we aspire for predicted probabilities that are constrained within the interval of 0 to 1. This ensures that the probabilities are meaningful and can be interpreted appropriately. Achieving this requires alternative methodologies that inherently account for the bounds of probability.

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