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458 Chapter 11 Regression with a Binary Dependent Variable

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Review the Concepts

11.1 Suppose that a linear probability model yields a predicted value of Y that
is equal to 1.3. Explain why this is nonsensical.

11.2 In Table 11.2 the estimated coefficient on black is 0.084 in column (1),
0.688 in column (2), and 0.389 in column (3). In spite of these large differ-
ences, all three models yield similar estimates of the marginal effect of race
on the probability of mortgage denial. How can this be?

11.3 What is a maximum likelihood estimation? What are the advantages
of using maximum likelihood estimators such as the probit and the
logit, instead of the linear probability model? How would you choose
between the probit and the logit?

11.4 What measures of fit are typically used to assess binary dependent variable
regression models?

Exercises

Exercises 11.1 through 11.5 are based on the following scenario: 700 income-earning
individuals from a district were randomly selected and asked whether they were
employed by the government (Govi = 1) or failed their entrance test (Govi = 0);
data were also collected on their gender (Malei = 1) if male and = 0 if female)
and their years of schooling (Schoolingi, in years). The following table summarizes
several estimated models.

11.1 Using the results in column (1):

a. Does the probability of working in the government depend on
Schooling? Explain.