The Basics
- Simple definition: A statistical model used when the outcome is binary (yes/no, buy/don’t buy).
- Core idea: Predicts the probability of an event occurring.
- Think of it as: Turning yes/no questions into probability estimates.
What It Actually Means
Logit models (logistic regression) handle situations where the dependent variable is categorical – often binary. Unlike linear regression, they constrain predictions between 0 and 1, making them suitable for probabilities. The model uses a logistic function to relate independent variables to the probability of an outcome. Coefficients show how variables affect the odds of the event.
Example
A researcher studies what factors determine whether a Pakistani household has a bank account (yes/no). Variables include income, education, and location. A logit model estimates how each factor affects the probability of account ownership.
Why It Matters
Logit models are everywhere in economics and social science: labor force participation, loan default, program participation, and voting behavior. They’re essential for understanding binary choices.
Don’t Confuse With
Probit Model – similar but uses a different distribution (normal vs. logistic). Results are usually very similar.
See also
Probit Model • Binary Choice • Logistic Regression • Econometrics • Maximum Likelihood Estimation
Read more about this with MASEconomics:
Logit and Probit Models: Understanding Binary Choice in Econometrics