Linear Dependent Variables

We will see some of the features of linear dependent variables and what best models can be adopted to estimate the parameters and regressors.

Linear Probability Model, this is the simplest choice in the class of the linear dependent variables class fo models and they simply estimate from OLS regression. The main disadvantage being that the probability can exceed 0 or 1, but we know that, . Also the error term,

Due to the heteroskedasticity nature of the error, we need heteroskedasticity-robust standard errors and also as the dependent variable is always evaluated at probability of 1, ie., , for values exceeding 0 or 1, they are truncated and many true values cannot be directly estimated at the extreme ends.

We will another class of model, namely Tobit and we can use this in class of censored and truncated models.