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[编程问题求助] 请问使用循环语句分年度回归求残差出现invalid syntax该如何解决 [推广有奖]

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akira1214 发表于 2019-4-11 23:46:58 |AI写论文

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如题，计算盈余管理程度时，使用循环语句分年度回归求残差，运行后显示invalid syntax r(198); 请问大佬们这是什么原因呢？该如何解决？非常感谢！！！
我的命令如下：
egen t = group(year)
sum t
local T = r(max)
gen Et = .
forvalues i=1/`T'{
qui reg tacc con changerev PPE if t==`i'
qui predict r if e(sample), res
qui replace Et = r if e(sample)
drop r
}

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关键词：盈余管理程度如何解决盈余管理管理程度如何解

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沙发

akira1214 发表于 2019-4-12 14:22:22

捞一下我自己QAQ

藤椅

黃河泉

发表于 2019-4-12 14:53:22

你若要问程序，永远附上相关资料；若附上资料，永远用 dataex 印出资料。
1. 先 ssc install dataex (并见说明)，将原始 Stata 资料中具有”代表性”的一部分资料列出，以供有意回答者实验之用，并能提供具体操作指令。
2. 请参考说明 https://bbs.pinggu.org/thread-5048204-1-1.html。

板凳

akira1214 发表于 2019-4-12 19:00:21

黃河泉发表于 2019-4-12 14:53
你若要问程序，永远附上相关资料；若附上资料，永远用 dataex 印出资料。
1. 先 ssc install dataex (并见 ...

老师您好，麻烦您指正，非常感谢！

* Example generated by -dataex-. To install: ssc install dataex
clear
input double(year code con tacc detaREV PPE) float(t Et)
2015 38 1.4635018937405268e-09 -.09081375185920101 .13498986117894807 .00009463786218439397 1 .
2016 38 1.342682817873079e-09 -.15500096256085322 .9717874229154585 .1962716913980428 2 .
2017 38 1.5640583007479837e-10 .06761289449732058 .0752578260663417 .016909102487020537 3 .
2015 58 3.7597974895121015e-10 .04527632898873016 .03459267847397709 .014108423985033453 1 .
2016 58 3.824588280885715e-10 .10030380038081288 -.03180746829075975 .014985422946805233 2 .
2017 58 3.9242213005285157e-10 -.0782340050736081 .49913650884719757 .02448909868183522 3 .
2015 156 1.799150969300986e-10 -.12130144839942131 .08041413194294603 .4168667274115883 1 .
2016 156 7.889547674910453e-11 -.06979727042562799 .017563071594725112 .1944185615163232 2 .
2017 156 7.126140757951971e-11 -.04435294478890935 .009130292896227911 .18944589519156185 3 .
2015 607 5.120451589997571e-10 .11435522195867064 .03522384457883521 .13357606997062624 1 .
2016 607 4.175713928812246e-10 -.0018206958186420678 .11613240180039164 .09794671375283737 2 .
2017 607 3.3671664866175197e-10 .008381750589108821 .000041549625632120395 .07847126282939515 3 .
2015 665 1.3306196788581183e-10 -.10471444382036818 .08339216253311832 .5739816195998082 1 .
2016 665 1.3148834436660043e-10 -.09915719624690957 .009773339688018556 .6056459760217124 2 .
2017 665 1.2315905577532639e-10 -.07297126610987145 .015999543933247585 .6250372535155878 3 .
2015 673 8.268669004620204e-09 5.007694068280846 3.8911001606935365 .035492813871218935 1 .
2016 673 3.8976219964402214e-10 .11052828659407588 .19197493332399468 .011763293793254179 2 .
2017 673 3.173833895806434e-10 .19714972471447492 -.05245524424487429 .02327685185694641 3 .
2015 676 1.5900091959665328e-09 -.056535749945884596 -.08823350307189716 .1545170649166614 1 .
2016 676 3.1993195350388516e-09 .4757882415316924 6.547164323907046 .03039416629672223 2 .
2017 676 1.5405295238573037e-10 .08312683643156077 .6179657491670661 .0013714679749096297 3 .
2015 681 6.562951260761847e-10 .033449485736116336 .09963247866257405 .005299471264435049 1 .
2016 681 3.609945648585683e-10 -.0014175886795263165 .06952556009383837 .0021815192552122015 2 .
2017 681 2.838033570148581e-10 .017720381691458075 .022441332632133604 .0016862932026715178 3 .
2015 719 1.1660810438844897e-10 -.024242178064376604 .004205255768504795 .13744829059722113 1 .
2016 719 1.0754242639142276e-10 -.003262117027670124 .0807717426620299 .13003023286287713 2 .
2017 719 9.853852825242149e-11 -.013532339106826479 .028062731464517713 .13901671351168138 3 .
2015 793 9.228924420553249e-11 .005637036989468914 .03380065001907262 .1105446478026152 1 .
2016 793 7.810939305230514e-11 .026688494389450812 .018457824211880376 .09691958087991175 2 .
2017 793 7.476019496261314e-11 -.017583159576554052 -.08458407268554345 .04600360262907385 3 .
2015 802 6.637144200415281e-10 -.0204071181119062 -.047350869974837076 .1342498052540299 1 .
2016 802 7.028662925371474e-10 .3682413758064102 .405692781784263 .03194648162360401 2 .
2017 802 1.895151698770165e-10 .13545541314252504 .07475449368667922 .007483014934970574 3 .
2015 829 9.139245419930555e-11 -.15658532218062363 .7707383455233096 .033563060047115525 1 .
2016 829 8.936432645375417e-11 .12210777817458723 -.820801795438251 .028669420927394353 2 .
2017 829 8.481923790804446e-11 -.06917110221925742 .49046561759846624 .02446617747611494 3 .
2015 835 2.216233035357269e-09 -.15745125482326147 -.2026875472542465 .3710106287520969 1 .
2016 835 6.778861477858827e-10 -.09750558056999789 -.02082462798269283 .09564756685412812 2 .
2017 835 7.279086942062217e-10 -.047473065662488 -.026000672119209655 .10347799030028287 3 .
2015 888 4.5349091757324276e-10 -.03551997535037128 .032577356500661875 .5834131851784375 1 .
2016 888 4.0510327865537786e-10 -.023780992021979686 -.009690537246199758 .5100335294663126 2 .
2017 888 3.7942844876878113e-10 -.056213517778880666 .01419355753879835 .5294480084904762 3 .
2015 892 3.242856302123483e-07 .7222887389700566 4.357040515377553 0 1 .
2016 892 1.1490367386020657e-07 25.794078996910045 83.31858840211588 4.435840013051586 2 .
2017 892 3.044309744259598e-10 .25998286731746156 .2522702287453741 .01125091964720065 3 .
2015 917 6.339727666648106e-11 -.006912613608265213 .03242594595647824 .3814474251831442 1 .
2016 917 4.9621456724371503e-11 .038279203729696315 .07448398525620695 .3055150197246071 2 .
2017 917 4.441125954251616e-11 -.016209132062218492 .05574011472138821 .30526741868936075 3 .
2015 2027 1.6926990524275276e-09 1.2785277987274062 13.932205006223837 .5089558880610933 1 .
2016 2027 7.998932592979523e-11 -.02816892170975776 .1268408922938533 .021303851022870208 2 .
2017 2027 8.244662109475095e-11 .14981709915367056 .14843845433989017 .028958950116243286 3 .
end

复制代码

我的命令如下：
sort code year
egen t = group(year)
sum t
local T = r(max)
gen Et = .
forvalues i=1/`T'{
qui reg tacc con changerev PPE if t==`i'
qui predict r if e(sample), res
qui replace Et=r if e(sample)
drop r
}