1. Regression with a Binary Dependent Variable
Chapter 11
Regression with a Binary Dependent Variable

2. Regression with a Binary Dependent Variable (SW Chapter 11)

3. Example: Mortgage denial and race The Boston Fed HMDA data set

4. The Linear Probability Model (SW Section 11.1)

5. The linear probability model, ctd.

6. The linear probability model, ctd.

7. Example: linear probability model, HMDA data

8. Linear probability model: HMDA data, ctd.

9. Linear probability model: HMDA data, ctd

10. The linear probability model: Summary

11. Probit and Logit Regression (SW Section 11.2)

15. Probit regression, ctd.

16. STATA Example: HMDA data

17. STATA Example: HMDA data, ctd.

18. Probit regression with multiple regressors

19. STATA Example: HMDA data

20. STATA Example, ctd.: predicted probit probabilities

21. STATA Example, ctd.

22. Logit Regression

23. Logit regression, ctd.

24. STATA Example: HMDA data

25. Predicted probabilities from estimated probit and logit models usually are (as usual) very close in this application.

26. Example for class discussion:

29. Hezbollah militants example, ctd.

30. Predicted change in probabilities:

31. Estimation and Inference in Probit (and Logit) Models (SW Section 11

32. Probit estimation by nonlinear least squares

33. Probit estimation by maximum likelihood

34. Special case: the probit MLE with no X

38. The MLE in the “no-X” case (Bernoulli distribution), ctd.:

39. The MLE in the “no-X” case (Bernoulli distribution), ctd:

40. The probit likelihood with one X

41. The probit likelihood function:

42. The Probit MLE, ctd.

43. The logit likelihood with one X

44. Measures of fit for logit and probit

45. Application to the Boston HMDA Data (SW Section 11.4)

46. The HMDA Data Set

47. The loan officer’s decision

48. Regression specifications

52. Table 11.2, ctd.

53. Table 11.2, ctd.

54. Summary of Empirical Results

55. Remaining threats to internal, external validity

56. Summary (SW Section 11.5)