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分组均值的检验

分组均值的检验

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我是要将变量stable2=1时对应的ist按照其高低分成3个组,第二步再在每个组里面分别按照rds,rda,rdcm,rdce这四个指标(rd的四个指标)的高低分成3组。然后求出每个组里面股票收益率的均值(变量r),并且检验每个组 ...
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我是要将变量stable2=1时对应的ist按照其高低分成3个组,第二步再在每个组里面分别按照rds,rda,rdcm,rdce这四个指标(rd的四个指标)的高低分成3组。然后求出每个组里面股票收益率的均值(变量r),并且检验每个组的均值r是否为0,以及在每一个ist水平下,高rd指标组的股票收益均值减去低rd指标组的股票收益均值的差异是否显著检验。
之前提问过是回归的呀,这次是要分组求均值,并检验,主要是两个检验不会做啊。学谢谢各位老师了。希望论文可以快点写完。
就是要做个这种表格:

按照RDA分组

P1

P2

P3

P3-P1


数据如下:
  1. * Example generated by -dataex-. To install: ssc install dataex
  2. clear
  3. input double r float stable2 double(ist rda rds rdcm rdce)
  4. .01834183333333333 0 .0334.015039411886228607 .0406 .0043508565160062.4021831417228706
  5. -.03236141666666667 0.15410000000000001 . .0027 .0004730511019202546 .
  6. .016430333333333335 0 .1399 .0022468628891426172 .0064 .00225063609018954 .
  7. .008628545454545456 0.10432999999999999.003934857004953578 .0129.005455884906601843 .08105974053480479
  8. .07756427272727273 1 .03927.005098979776592662 .0167.005916413678691346 .10824707585863298
  9. .012225833333333325 1 .02418 .00800057284556839 .0273 .0061070494507257395 .18308748426222066
  10. .13823000000000002 0 .00735.004238416072819286 .012.002094412310655742.6808627833422383
  11. .07662258333333334 0 .0016 . . .13909421836262895 .
  12. -.04288216666666666 0.10529999999999999 .0051744413470747016 .0083.002563389735739812 .
  13. .01596875 0 .0355.009142403155317141 .0181.011955337800977633 .
  14. -.020998500000000003 0 .056400000000000006 .01135827015599402 .0233.015139874213726073 .11707964834283839
  15. .06766741666666666 1 .045599999999999995.012666071185887764.024700000000000003 .0180143821530038 .08914823718899387
  16. -.0004263333333333347 1 .015600000000000001.013768779480464543 .0296.017971590588343456 .10994590426975569
  17. -.012101833333333334 1 .1822.015489943285921461 .0323.013781840889958189 .27707477048072415
  18. -.008630499999999996 0 .009300000000000001.009662825531498413.009300000000000001.055721501118698946.3547101437131713
  19. -.012830999999999997 0 .0169.010038128912963223 .0085 .08952247643809319.4341437024560135
  20. .03541249999999999 0 .006999999999999999.012509617758897489 .0113 .1092959568684015 .901778600824899
  21. .1897291666666667 1 .0038 .01460940392733124 .0115 .09932323093719997.7939769070899702
  22. -.05900040000000002 1.08039999999999999 .01305174985464185 .0113 .06094352922213424 .29162674026660135
  23. .0023835000000000006 1 .0378.016097639616157684 .0125.027668813188941186.9799262692190663
  24. -.03601075 0.18299999999999997.033141294543842424 .0332.027169348318120425 .
  25. .014828999999999995 0 .1909.014963598419482223.058899999999999994.010721013463488357 .13003449507469597
  26. .08494249999999999 0 .2332.010206498696354985 .0311.008866176118467462 .08668969790670267
  27. .06015725 0.24969999999999998 .01906224333294155.059800000000000006.011346151676048452 .40710496261149187
  28. -.028439250000000003 0 .5597 .0074962438452874924 .0024 .0032299461354048753 .12470501796920216
  29. -.005757750000000002 0 .5204.007600150027047148 .0052.011620058417705675 .29714346884538445
  30. .030523583333333337 0 .5248 .01463342054392146 .0091 .02400328002013226.5119501139493656
  31. .08757491666666667 1 .52411.010862464494180919.009899999999999999.021428688573095565.5822567826386453
  32. .001065499999999997 1 .4915.010417025016676393 .0092.014263866664070566 .29153773572209674
  33. .13253939999999997 0 .05132 . ..000970553473717573 .
  34. -.02147675 0 .53496.004746656939137576.006500000000000001.008218824843741158 .18532680672950333
  35. -.029452083333333334 0 .5566500000000001.008227865013707621 .0077 .01633161343162522 .15816975668805858
  36. .023135666666666665 0 .09721.009384194864302935 .0103 .02628516117066643 .26841630980718706
  37. .07903966666666668 1 .039900000000000005.010450437777837298 .012.015961953432874317 .31261314800260365
  38. -.012319249999999999 1 .0301.013549059178515309 .0151.015319170755153282 .26684208110220625
  39. .00930325 1 .0106 .00882605948487893.011899999999999999.007356024234541848 .16271337836657557
  40. .013799 0 .1199 . . .0007422812437233498 .
  41. .11122458333333335 0.25923999999999997 . .0015 .0035290747874558016 .
  42. -.04272791666666668 0 .26315 . .0018 .00211581247043553 .
  43. .006930249999999998 1.27740000000000004 . .003.002333902238580387 .
  44. .020966583333333327 0 .5343.008376804055774585 .004 .0068633227881769706 .29681515643718764
  45. .0005323333333333326 0 .5579999999999999.008285848943873336 .003 .01275427471368546 .31923139404921497
  46. .03632483333333334 0 .5318.008645467488881094 .0045000000000000005.009760265900021088.4151791353672393
  47. .05987275 1 .5113.007238916660631947 .0038.007411783298490095.5318883737328722
  48. -.011949500000000004 1 .6056.006330693242390625 .0034000000000000002.007294692959172277 .23701002639718569
  49. .01973575 1 .6099 .00553400361806091 .0028000000000000004.004740707755885467 .384269880996891
  50. .05422975 0 .1524 .01596349565269857 .0217 .1009251241495685.6406988058762047
  51. -.02665099999999999 0.16036999999999998.017147729019341175.024900000000000002.026322517775823746.8482431539569288
  52. .022074833333333335 0 .1818.014373579189573524 .0268.020315730329716973 .899727795678346
  53. .025263500000000005 0 .0271.002504880157106371 .0024.002945542432899536.3942485114756091
  54. end
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