How do I choose probit or logit?

The choice between logit and probit often depends on the nature of the data and the underlying assumptions of the model. Logit is preferred when modeling the probability of an event directly, as it provides a more intuitive interpretation of the coefficients in terms of log odds.

In contrast, probit is preferred when the binary outcome is believed to depend on an underlying Gaussian variable. This assumption allows for a more direct interpretation of the latent variable, which can be useful in certain applications.

Logit and probit models are two commonly used methods in statistical analysis, particularly in the field of econometrics. Each model employs a different function to estimate the probability of an event occurring.

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Logit utilizes a logistic function, which is characterized by an S-shaped curve that ranges between 0 and 1. This function is well-suited for modeling the log odds or probability of an event directly, as it naturally bounds the output within the valid probability range.