Lecture_17__Prof._Arkonac's_Slides_(Sum_Panel_Ch11)

Lecture_17__Prof._Arkonac's_Slides_(Sum_Panel_Ch11) -...

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Regression with a Binary Dependent Variable I & II (Fall 2010) Lecture 17 Prof: Seyhan Erden Arkonac, PhD Problem Set 6 is due NOW! Solutions will be posted tomorrow evening. 1
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. list year state vfrall beertax y83 +-----------------------------------------+ | year state vfrall beertax y83 | |------------------------------------------| 1. | 1982 AL .212836 1.539379 0 | 2. | 1983 AL .234848 1.788991 1 | 3. | 1984 AL .233643 1.714286 0 | 4. | 1985 AL .219348 1.652542 0 | 5. | 1986 AL .266914 1.609907 0 | 6. | 1987 AL .271859 1.56 0 | 7. | 1988 AL .249391 1.501444 0 | 8. | 1982 AZ .249914 .2147971 0 | 9. | 1983 AZ .226738 .206422 1 | 10. | 1984 AZ .282878 .2967033 0 | 11. | 1985 AZ .280201 .3813559 0 | 12. | 1986 AZ .307106 .371517 0 | 13. | 1987 AZ .276728 .36 0 | 14. | 1988 AZ .270565 .346487 0 | 15. | 1982 AR .238405 .650358 0 | 16. | 1983 AR .23957 .6754587 1 | 17. | 1984 AR .223785 .5989011 0 | 18. | 1985 AR .226367 .5773305 0 | 19. | 1986 AR .254323 .5624355 0 | 20. | 1987 AR .267588 .545 0 | 21. | 1988 AR .254697 .5245429 0 | 22. | 1982 CA .186194 .1073986 0 | 23. | 1983 CA .180672 .103211 1 | 24. | 1984 CA .194611 .0989011 0 | 25. | 1985 CA .188128 .095339 0 | 26. | 1986 CA .194548 .0928793 0 | 27. | 1987 CA .198966 .09 0 | 28. | 1988 CA .190365 .0866218 0 | 29. | 1982 CO .217448 .2147971 0 | 2
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26. | 1986 CA .194548 .0928793 0 | 27. | 1987 CA .198966 .09 0 | 28. | 1988 CA .190365 .0866218 0 | 29. | 1982 CO .217448 .2147971 0 | 30. | 1983 CO .205144 .206422 1 | 31. | 1984 CO .190596 .1978022 0 | 32. | 1985 CO .179201 .190678 0 | 33. | 1986 CO .18463 .1857585 0 | 34. | 1987 CO .179308 .18 0 | 35. | 1988 CO .15056 .1732435 0 | 36. | 1982 CT .164695 .2243437 0 | 37. | 1983 CT .13949 .2335631 1 | 38. | 1984 CT .148653 .248011 0 | 39. | 1985 CT .141147 .2390784 0 | 40. | 1986 CT .140933 .2329102 0 | 41. | 1987 CT .139832 .22569 0 | 42. | 1988 CT .149706 .2172185 0 | 43. | 1982 DE .203333 .173031 0 | 44. | 1983 DE .181518 .1662844 1 | 45. | 1984 DE .211726 .1593406 0 | 46. | 1985 DE .167203 .1536017 0 | 47. | 1986 DE .21485 .1496388 0 | 48. | 1987 DE .226708 .145 0 | 49. | 1988 DE .242424 .1395573 0 | 50. | 1982 FL .253197 1.073986 0 | 51. | 1983 FL .249768 1.170413 1 | 52. | 1984 FL .254661 1.186813 0 | 53. | 1985 FL .249164 1.144068 0 | 54. | 1986 FL .242004 1.114551 0 | 55. | 1987 FL .236131 1.08 0 | 56. | 1988 FL .249534 1.039461 0 | 3
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4 Combined entity and time fixed effects When T = 2, computing the first difference and including an intercept is equivalent (gives exactly the same regression) as the previous STATA command. So there are various equivalent ways to allow for both entity and time fixed effects: differences & intercept ( T = 2 only) – this is what we did initially entity demeaning & T – 1 time indicators time demeaning & n – 1 entity indicators T – 1 time indicators & n – 1 entity indicators entity & time demeaning
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Effect of seatbelt usage on fatality rate : (1) Regular OLS: . reg fatalityrate sb_useage speed65 speed70 ba08 drinkage21 lnincome age Source | SS df MS Number of obs = 556 -------------+------------------------------ F( 7, 548) = 95.41 Model | .007711649 7 .001101664 Prob > F = 0.0000 Residual | .006327757 548 .000011547 R-squared = 0.5493 -------------+------------------------------ Adj R-squared = 0.5435 Total | .014039406 555 .000025296 Root MSE = .0034 ------------------------------------------------------------------------------ fatalityrate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- sb_useage | .0040684 .0012158 3.35 0.001 .0016803 .0064565 speed65 | .0001479 .0004029 0.37 0.714 -.0006436 .0009394 speed70 | .0024045 .0005112 4.70 0.000 .0014003 .0034086 ba08 | -.0019246 .0004447 -4.33 0.000 -.0027982 -.001051 drinkage21 | .0000799 .0008756 0.09 0.927 -.0016401 .0017998 lnincome | -.0181444 .0009311 -19.49 0.000 -.0199733 -.0163155 age | -7.22e-06 .0001089 -0.07 0.947 -.0002212 .0002067 _cons | .1965469 .0082232 23.90 0.000 .1803941 .2126998 ------------------------------------------------------------------------------ 5
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(2) With Fixed State Effects: . areg fatalityrate sb_useage speed65 speed70 ba08 drinkage21 lnincime age, absorb(state) r cluster(state) Linear regression, absorbing indicators Number of obs = 556 F( 7, 50) = 87.90 Prob > F = 0.0000 R-squared = 0.8867 Adj R-squared = 0.8737 Root MSE = .00179 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust fatalityrate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- sb_useage | -.0057748 .001751 -3.30 0.002 -.0092919 -.0022577 speed65 | -.000425 .0004778 -0.89 0.378 -.0013847 .0005346 speed70 | .0012333 .0003654 3.38 0.001 .0004994 .0019671 ba08 | -.0013775 .0003935 -3.50 0.001 -.0021677 -.0005872 drinkage21 | .0007453 .0007536 0.99 0.327 -.0007684 .002259 lnincome | -.0135144 .0025018 -5.40 0.000 -.0185394 -.0084894 age | .0009787 .0007826 1.25 0.217 -.0005933 .0025507 _cons | .1209958 .0193262 6.26 0.000 .082178 .1598137 -------------+---------------------------------------------------------------- state | absorbed (51 categories) 6
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(3) With Fixed Time and Fixed State Effects (combined): . areg fatalityrate sb_useage speed65 speed70 ba08 drinkage21 lnincime age y83 y84 y85 y86 y87 y88 y89 y90 y91 y92 y93 y94 y95 y96, absorb(state) r cluster(state) Linear regression, absorbing indicators Number of obs = 556 F( 21, 50) = 47.41 Prob > F = 0.0000 R-squared = 0.9098 Adj R-squared = 0.8966 Root MSE = .00162 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust fatalityrate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- sb_useage | -.0037186 .0015246 -2.44 0.018 -.0067808 -.0006563 speed65 | -.0007833 .0006093 -1.29 0.205 -.0020071 .0004405 speed70 | .0008042 .0004803 1.67 0.100 -.0001605 .0017688 ba08 | -.0008225 .0004656 -1.77 0.083 -.0017577 .0001127 drinkage21 | -.0011337 .0006534 -1.74 0.089 -.0024461 .0001787 lnincome | .0062643 .0070367 0.89 0.378 -.0078693 .0203979 age | .001318 .0007287 1.81 0.076 -.0001455 .0027816 7
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This note was uploaded on 11/10/2011 for the course ECON 3142 taught by Professor Arkonac during the Spring '11 term at Columbia.

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Lecture_17__Prof._Arkonac's_Slides_(Sum_Panel_Ch11) -...

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