What condition will fail to hold in a linear probability model?
A) The determining function will be linear in parameters.
B) The moment conditions will define the estimate of the coefficients.
C) The errors will be homoscedastic.
D) The coefficient estimates will minimize the sum of squared residuals.

Question

Quizplus · Accepted Answer

In a linear probability model, the dependent variable is binary (i.e. takes on a value of either 0 or 1), which means the errors will be heteroscedastic (i.e. the variance of the errors will not be constant across all values of the independent variables). Therefore, option C, which states that the errors will be homoscedastic, will fail to hold in a linear probability model. The other options (A, B, and D) are all correct conditions for a linear probability model.

What Condition Will Fail to Hold in a Linear Probability