The main difference between LPM (Linear Probability Model) and the logit model lies in the way the dependent variable is modeled.
1. Linear Probability Model (LPM):
The LPM assumes that the dependent variable is a binary (dichotomous) variable that takes the values of 0 or 1. It models the probability of the dependent variable being equal to 1 using a linear regression model. In other words, it assumes a linear relationship between the predictors and the probability of the binary outcome. The LPM does not account for the fact that probabilities cannot be negative or greater than 1, which can lead to issues with interpretation and misspecification.
2. Logit Model:
The logit model, also known as logistic regression, is a popular statistical model for binary outcomes. Unlike the LPM, the logit model models the log-odds (logarithm of the odds) of the dependent variable being equal to 1 as a linear combination of the predictors. It uses the logistic function to transform the linear combination into probabilities between 0 and 1. The logit model handles the non-linear relationship between the predictors and the probabilities of the binary outcome.
In summary, while both models are used to analyze binary outcomes, the LPM assumes a linear relationship between predictors and probabilities, whereas the logit model, based on the logistic function, accounts for the non-linear relationship between predictors and probabilities. The logit model is generally preferred due to its ability to handle non-linearities in the data and produce interpretable results.
Difference between LPM. And logit model
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