Question

What is the underlying idea behind the logit model? Explain how the parameters of the logit model can be estimated by maximum likelihood method.

31 Aug 2025
Answer :
Word Count : 1103
The logit model is one of the most widely used models in econometrics to analyze situations where the dependent variable is binary, meaning it can only take two possible outcomes such as success or failure, yes or no, employed or unemployed, and so on. The underlying idea behind the logit model is that the probability of the dependent variable taking a certain value can be expressed as a non-linear function of a linear combination of explanatory variables. Ordinary linear regression is not appropriate for such problems because it can predict probabilities outside the \[0,1] interval and because the variance of the error terms is heteroskedastic. The logit model resolves this by using a logistic cumulative distribution function to link the explanatory variables to the probability outcome, ensuring that the predicted values always lie within the unit interval. The model begins with the specification of a latent variable framework. Suppose we have an unobservable continuous latent variable $y_i^*$ that depends linearly on a set of covariates: $$ y_i^* = X_i \beta + u_i, $$ where $X_i$ is the vector of explanatory variables, $\beta$ is ________ __________ ____ ________ _____ _______.
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