I'm trying to understand the distinction between logit and probit LPM. I know they are both used in statistical modeling, but what sets them apart from each other in terms of their approach and application?
5 answers
Chiara
Sun Oct 13 2024
The shape of the predicted probabilities in logit/probit models also differs significantly from that of the LPM. While the LPM assumes a linear relationship between the dependent and independent variables, the logit/probit models exhibit a distinct, nonlinear pattern.
DongdaemunTrend
Sun Oct 13 2024
Specifically, the logit/probit curves are S-shaped, resembling a sigmoid function. This shape allows for more flexible modeling of probabilities, particularly when dealing with binary outcomes that are influenced by multiple factors.
CryptoVisionary
Sun Oct 13 2024
A key characteristic that sets logit/probit models apart from the Linear Probability Model (LPM) lies in their predicted probabilities.
GeishaMelody
Sun Oct 13 2024
In the case of logit/probit models, the predicted probability of an outcome equaling 1 is inherently bounded. It never dips below 0 or surpasses 1, ensuring that the predictions remain within a realistic and interpretable range.
Maria
Sun Oct 13 2024
This constraint is absent in the LPM, where predicted probabilities can theoretically take on any value, including those that are unrealistic or outside the 0 to 1 spectrum.