- * Example generated by -dataex-. To install: ssc install dataex
- clear
- input long id int year float industry double(A EBXI cfo) float(acc invA Dsale DAR DS_DAR PPE)
- 1 2007 18 352539361000 3721942000 17051576000 . . . . . .
- 1 2008 18 474440173000 803426000 24342611000 -.066770375 2.836563e-12 . . . .004751027
- 1 2009 18 587811034000 6159127000 32193611000 -.05487411 2.1077474e-12 . .04236502 . .0036136506
- 1 2010 18 727207076000 7849555000 21746295000 -.02364151 1.701227e-12 . -.005069224 . .0040698336
- 1 2011 18 1258176944000 13132701000 -14439373000 .03791502 1.375124e-12 . .20914483 . .004846302
- 1 2012 18 1606536760000 17541661000 185838461000 -.13376242 7.948007e-13 . -.06765286 . .0028107676
- 1 2013 18 1.891741e+12 1.9955e+10 9.1674e+10 -.04464199 6.22457e-13 . .05758484 . .002299356
- 1 2014 18 2.186459e+12 2.6246e+10 2.5321e+10 .0004889676 5.286136e-13 . .03407919 . .002015075
- 1 2015 18 2.507149e+12 2.8895e+10 -1.826e+09 .014050572 4.573605e-13 . .02656167 . .002189842
- 1 2016 18 2.953434e+12 2.9779e+10 1.0989e+10 .007494568 3.988594e-13 . .04211437 . .003316915
- 1 2017 18 3.248474e+12 3.0223e+10 -1.1878e+11 .05045076 3.385889e-13 . .0018158524 . .0027209006
- 1 2018 18 3.418592e+12 3.2305e+10 -5.7323e+10 .0275908 3.0783685e-13 . . . .003355114
- 2 2007 19 100094467908.29 7652897499.73 -10437715815.8 . . . . . .
- 2 2008 19 119236579721.09 6364789552.07 -34151830.4 .06392902 9.990562e-12 .0546001 .0005783719 .05402173 .012641385
- 2 2009 19 137608554829.39 8685082798 9253351319.55 -.004765891 8.386688e-12 .066164546 -.0017577067 .06792225 .011372156
- 2 2010 19 215637551741.83 11894885308.23 2237255451.45 .0701819 7.26699e-12 .013319218 .006401002 .006918216 .00886269
- 2 2011 19 296208440030.05 15763216697.19 3389424571.92 .05738236 4.637411e-12 .09770515 -.00036733295 .09807248 .007400672
- 2 2012 19 378801615075.37 21013040794.06 3725958472.52 .05836121 3.376001e-12 .10578191 .001254977 .10452694 .005442982
- 2 2013 19 479205323490.54 24261338387.14 1923868889.89 .05896878 2.6399044e-12 .08527563 .003147878 .08212775 .005622383
- 2 2014 19 508408755415.65 24979358867.78 41724819113.36 -.03494423 2.0867882e-12 .022890424 -.002472631 .025363056 .004817041
- 2 2015 19 611295567689.29 33122777302.51 16046020691.5 .033588637 1.9669212e-12 .09669606 .0012127672 .0954833 .009672294
- 2 2016 19 830674213924.14 39023778797.86 39566129021.69 -.0008872144 1.6358698e-12 .073496535 -.000712252 .07420879 .01114157
- 2 2017 19 1165346917804.55 50812916408.4 82322834216.5 -.03793294 1.2038414e-12 .002913144 -.0007734955 .003686639 .008545839
- 2 2018 19 1528579356474.81 67498612522.27 33618183388.52 .029073255 8.581135e-13 .04700937 .00013167474 .04687769 .00989731
- 4 2007 11 169822593.95 -9619521.35 -6952156.3 . . . . . .
- 4 2008 11 167810585.24 -10347869.22 1428298.18 -.06934394 5.888498e-09 .016872216 -.013155826 .03002804 .16989303
- 4 2009 11 230512058.69 8463829.54 46596046.56 -.2272337 5.9591e-09 .09990893 .011114977 .08879395 .15802544
- 4 2010 11 184219435.7 24718721.39 -11737579.58 .15815355 4.338168e-09 .3090983 -.011123657 .320222 .10570723
- 4 2011 11 196307219.48 12393007.62 19365739.89 -.03785015 5.428309e-09 -.3084787 .001323359 -.3098021 .1987829
- 4 2012 11 192913568.86 14003591.85 17562720.98 -.018130403 5.094056e-09 .116448 .018281378 .09816662 .236997
- 4 2013 11 242152079.61 11487797.5 -21042308.56 .1686253 5.183669e-09 -.12740801 -.02264414 -.10476387 .2129636
- 4 2014 11 338282581.12 3805326.19 -5534050.66 .03856823 4.129636e-09 .032311317 -.004206745 .03651806 .1561878
- 4 2015 11 398673507.14 10137004.02 9261869.98 .002586991 2.9561085e-09 .11778792 -.0030020976 .12079002 .11168975
- 4 2016 11 223716293.01 51576371.77 147929759.13 -.24168496 2.508318e-09 .4194299 -.0000509255 .4194809 .08394159
- 4 2017 11 268844295.64 8796946.37 14439484.24 -.025221845 4.469947e-09 -.666309 .03464385 -.7009528 .1395556
- 4 2018 11 351177470.17 -23860747.44 -53310246.02 .1095411 3.719625e-09 .8490527 .0662529 .7827998 .00368339
- 5 2007 20 1457023884.59 59877047.42 3146854.55 . . . . . .
- 5 2008 20 1389031247.96 -34416514.94 -126053753.62 .062893435 6.863305e-10 -.13061649 -.015066937 -.11554955 .05587
- 5 2009 20 1319203895.55 -81190360.03 8131607.07 -.06430522 7.199262e-10 -.04717517 -.011346314 -.03582886 .06226491
- 5 2010 20 1293608601.28 -10606219.67 6126214.55 -.012683736 7.580329e-10 .071286745 .007064779 .06422196 .05897978
- 5 2011 20 1260586163.19 -66233064.1 34773367.82 -.07808114 7.730314e-10 -.05697194 -.007311158 -.04966078 .020282485
- 5 2012 20 1319702418.01 -4649623.73 179474907.37 -.14606263 7.932818e-10 .028468017 .11030674 -.08183873 .019085474
- 5 2013 20 1188653949.18 -44717841.08 -111718142.35 .05076925 7.577466e-10 -.03588652 -.10501061 .069124095 .01877797
- 5 2014 20 1366663807.86 53515574.98 -9177753.18 .05274313 8.412878e-10 .000601766 8.839747e-07 .000600882 .0218075
- 5 2015 20 2194008790.39 -59377412.59 15794819.68 -.05500419 7.317089e-10 .022938324 .0528598 -.02992148 .02725711
- 5 2016 20 2527235374.1 129010582.49 -185863029.98 .1435152 4.557867e-10 .18128344 .03998636 .14129709 .017131679
- 5 2017 20 2912099984.28 55770681.54 -243303474.04 .11834044 3.956893e-10 .01941204 .012369726 .007042315 .013013029
- 5 2018 20 3121423378.81 172744390.03 -94794814.73 .09187157 3.433948e-10 -.015363914 .036671594 -.05203551 .00965912
- 6 2007 19 5568431161.83 285021368.53 -804871334.73 . . . . . .
- 6 2008 19 5921442329.83 159197461.82 -1364121666.06 .27356344 1.795838e-10 -.0004119091 .0000880715 -.00049998064 .005049209
- end
以上是数据
以下是我的命令
tsset id year
drop if A==.|EBXI==.|cfo==.|acc==.|invA==.|Dsale==.|DAR==.|DS_DAR==.|PPE==.
keep id year industry A EBXI cfo acc invA Dsale DAR DS_DAR PPE
egen industry_year=group(industry year),label lname(industry_year)
qui sum industry_year
global N = r(max)
local xx "invA DS_DAR PPE"
gen DACC=.
forvalues i=1/$N{
qui reg acc `xx' if (industry_year==`i'), nocons
qui predict e if e(sample),res
qui replace DACC =e if e(sample)
drop e
}
为什么会出现insufficient observations的情况呢,求解答!谢谢大家!


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