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 will not be constant for all values of X, while (strictly) linear probability models' marginal effects will be constant.
B. Probit/logit marginal effects are stable, while linear probability models tend to be noisier.
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 models' marginal effects are causal; linear probability models are not.
Answer: A
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