(Ch.7 9) Let d be a dummy (binary) variable and let z be a
quantitative variable. Consider the model y = β0 + δ0d + β1z +
δ1d∗z + u; this is a general version of a model with an interaction
between a dummy variable and a quantitative variable. [An example
is in equation (7.17).] (5 pts each)
(i) Since it changes nothing important, set the error to zero, u =
0. Then, when d = 0 we can write the relationship between y and z
as the function f0(z) = β0 + β1z. Write the same relationship when
d = 1, where you should use f1(z) on the left-hand side to denote
the linear function of z. (ii) Assuming that δ 6= 0 (which means
the two lines are not parallel), show that the value of z∗ such
that f0(z∗) = f1(z∗) is z∗ = −δ0/δ1. This is the point at which the
two lines intersect [as in Figure 7.2(b)]. Argue that z∗ is
positive if and only if δ0 and δ1 have opposite signs. (iii) Using
the data in TWOYEAR, the following equation can be estimated: \log
(wage) = 2.289−.357female + .50totcoll + .030female∗totcoll (0.011)
(.015) (.003) (.005) n = 6,763, R2 = .202
where all coefficients and standard errors have been rounded to three
decimal places. Using this equation, find the value of totcoll such
that the predicted values of log(wage) are the same for men and
women. (iv) Based on the equation in part (iii), can women
realistically get enough years of college so that their earnings
catch up to those of men? Explain.
(Ch.7 9) Let d be a dummy (binary) variable and let z be a quantitative variable....