Use the data in LOANAPP.RAW for this exercise. The binary variable to be explained is approve, which is equal to one if a mortgage loan to an individual was approved. The key explanatory variable is white, a dummy variable equal to one if the applicant was white. The other applicants in the data set are black and Hispanic.
To test for discrimination in the mortgage loan market, a linear probability model can be used:
approve = (0 + (1 white + other factors.
(i) If there is discrimination against minorities, and the appropriate factors have been controlled for, what is the sign of (1?
(ii) Regress approve on white and report the results in the usual form. Interpret the coefficient on white. Is it statistically significant? Is it practically large?
(iii) As controls, add the variables hrat, obrat, loanprc, unem, male, married, dep, sen, cosign, chist, pubrec, mortlatl, mortlat2, and vr. What happens to the coefficient on white? Is there still evidence of discrimination against nonwhites?
(iv) Now, allow the effect of race to interact with the variable measuring other obligations as a percentage of income (obrat). Is the interaction term significant?
(v) Using the model from part (iv), what is the effect of being white on the probability of approval when obrat = 32, which is roughly the mean value in the sample? Obtain a 95% confidence interval for this effect.
1) You can buy this solution for 0,5$.
2) The solution will be in 8 hours.
3) If you want the solution will be free for all following visitors.
4) The link for payment paypal.me/0,5usd
5) After payment, please report the number of the task to the oneplus2014@gmail.com
New search. (Also 1294 free access solutions)
Use search in keywords. (words through a space in any order)