1. Instrumental Variables Regression
Chapter 10
Instrumental Variables Regression

2. Instrumental Variables Regression (SW Chapter 10)

3. IV Regression with One Regressor and One Instrument (SW Section 10.1)

4. Terminology: endogeneity and exogeneity

5. Two conditions for a valid instrument

6. The IV Estimator, one X and one Z

7. Two Stage Least Squares, ctd.

8. Two Stage Least Squares, ctd.

9. The IV Estimator, one X and one Z, ctd.

10. The IV Estimator, one X and one Z, ctd.

11. Consistency of the TSLS estimator

12. Example #1: Supply and demand for butter

16. TSLS in the supply-demand example:

17. TSLS in the supply-demand example, ctd.

18. Example #2: Test scores and class size

19. Example #2: Test scores and class size, ctd.

20. Inference using TSLS

24. Inference using TSLS, ctd.

25. Example: Cigarette demand, ctd.

26. Cigarette demand, ctd.

27. STATA Example: Cigarette demand, First stage

28. Second stage

29. Combined into a single command

30. Summary of IV Regression with a Single X and Z

31. The General IV Regression Model (SW Section 10.2)

32. Identification

33. Identification, ctd.

34. The general IV regression model: Summary of jargon

35. TSLS with a single endogenous regressor

36. Example: Demand for cigarettes

37. Example: Cigarette demand, one instrument

38. Example: Cigarette demand, two instruments

40. The General Instrument Validity Assumptions

41. The IV Regression Assumptions

42. Checking Instrument Validity (SW Section 10.3)

43. Checking Assumption #1: Instrument Relevance

44. What are the consequences of weak instruments?

45. An example: the sampling distribution of the TSLS t-statistic with weak instruments

46. Why does our trusty normal approximation fail us?

47. Measuring the strength of instruments in practice: The first-stage F-statistic

48. Checking for weak instruments with a single X

49. What to do if you have weak instruments?

50. Confidence intervals with weak instruments

51. Estimation with weak instruments

52. Checking Assumption #2: Instrument Exogeneity

53. Testing overidentifying restrictions

56. Checking Instrument Validity: Summary

57. 2. Exogeneity

58. Application to the Demand for Cigarettes (SW Section 10.4)

59. Panel data set Estimation strategy
Annual cigarette consumption, average prices paid by end consumer (including tax), personal income
48 continental US states,
Estimation strategy
Having panel data allows us to control for unobserved state-level characteristics that enter the demand for cigarettes, as long as they don’t vary over time
But we still need to use IV estimation methods to handle the simultaneous causality bias that arises from the interaction of supply and demand.

60. Fixed-effects model of cigarette demand

61. The “changes” method (when T=2)

62. STATA: Cigarette demand

63. Use TSLS to estimate the demand elasticity by using the “10-year changes” specification

64. Check instrument relevance: compute first-stage F

65. Check instrument relevance: compute first-stage F

66. What about two instruments (cig-only tax, sales tax)?

67. Test the overidentifying restrictions

68. The correct degrees of freedom for the J-statistic is m–k:

69. Tabular summary of these results:

70. How should we interpret the J-test rejection?

71. The Demand for Cigarettes: Summary of Empirical Results

72. Assess the validity of the study

73. Finding IVs: Examples (SW Section 10.5)

74. Example: Cardiac Catheterization

75. Cardiac catheterization, ctd.

76. Cardiac catheterization, ctd.

77. Example: Crowding Out of Private Charitable Spending

78. Private charitable spending, ctd.

79. Private charitable spending, ctd.

80. Private charitable spending, ctd.

81. Example: School Competition

82. School competition, ctd.

83. School competition, ctd.

84. Summary: IV Regression (SW Section 10.6)

85. Some IV FAQs

87. Threats to internal validity of IV, ctd.