1. MPHIL AdvancedEconometrics
Lecture 12

2. Models for Discrete Choice
Greene Chapter 21
Gujarati Chapter 15

3. Introduction

4. Models for Binary Choice (Linear Probability Model)

5. Linear Probability Model

6. Non-Normality of Disturbances in Linear Probability Model

7. Heteroscedastic Disturbances in Linear Probability Model

9. R2 is No Longer a Good Measure of Fit

10. Logit Model

11. Logit Model

12. Logit Model

13. Logit Model

14. Analysis of Binary Choice Models using Matrices

15. Analysis of Binary Choice Models using Matrices

16. Reformulation of Logit Model

17. Estimation of the Logit Model

18. Estimation of the Logit Model

19. Estimation of the Logit Model

20. Estimation of the Logit Model

21. Estimation of the Logit Model

22. Grouped Data

23. Grouped Data

24. Grouped Data

25. Grouped Data

26. Grouped Data

27. Interpretation of Odds Ratio

28. Probit Model

29. Probit Model

30. Probit Model

31. Probit Model

32. Probit Model

33. Which Distribution Should We Use?

34. Marginal Effects

35. Marginal Effects

36. Marginal Effects

37. Comparing Logit and Probit Coefficients

38. Comparing Logit and Probit Coefficients

39. Comparing Logit and Probit Coefficients

40. Comparing Logit and Probit Coefficients

41. Tobit Model

42. Tobit Model

43. Tobit Model

45. Count Data

46. Count Data

47. Count Data

48. Count Data

49. Other Limited Dependent Variable Models (Ordered Logit and Probit)

50. Multinomial Logit and Probit

51. Duration Models