Which of the following correctly summarizes one of the differences in calculating marginal effects of probit/logit models relative to linear probability models?
A) Probit/logit models' marginal effects are causal; linear probability models are not.
B) Probit/logit models' marginal effects will not be constant for all values of X, while (strictly) linear probability models' marginal effects will be constant.
C) Probit/logit marginal effects cannot be positive since predictions need to be between zero and 1, while linear probability models can be positive.
D) Probit/logit marginal effects are stable, while linear probability models tend to be noisier.

Question

Quizplus · Accepted Answer

Probit/logit models use a non-linear transformation of the probability scale, which means that their marginal effects will vary depending on the level of X. In contrast, linear probability models assume a constant marginal effect.

Which of the Following Correctly Summarizes One of the Differences