论文数据是大N小T的面板数据,目的是研究利率对资本结构的影响,其余变量是控制变量。在这种情况下,需要考虑变量的内生性吗?如果需要,应该怎么设置工具变量呢?存在内生性,自相关性以及截面相关,我在论坛上看到您对于相关问题的解读,https://bbs.pinggu.org/thread-836414-1-1.html。但是我仍然处于一知半解的状态。
xtivreg2........., fe robust cluster(id)
(1)这样的估计就是GMM估计方法吗?
(2)以下论文表述方式正确吗?“由于模型存在异方差、自相关、截面面相关,故采用GMM估计方法估计变量系数。”
(3)估计结果中并没有截距项,论文中的回归方程应该如何写?(还是说不用写回归方程,直接使用回归系数进行经济意义的解释就行?)谢谢老师!
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- * Example generated by -dataex-. To install: ssc install dataex
- clear
- input double(tl size tang ndts npr absr1 absr2) byte(dr1 dr2) double(absdr1 absdr2)
- .326872911 26.88540136 .003530317 .003045651 .045272067 .19 .2175 1 1 .19 .2175
- .316136973 27.09967136 .002916687 .002782466 .351950274 -1.37 -1.59 0 0 0 0
- .221939271 27.31247711 .0032897 .002563306 .357810962 .080833333 .071666667 1 1 .080833333 .071666667
- .17558346 27.86068492 .002801089 .002176966 .357217126 .950833333 .874166667 1 1 .950833333 .874166667
- .159387682 28.1051019 .002201284 .001948966 .347219085 -.0625 -.076666667 0 0 0 0
- .067413861 28.26851869 .001952699 .001912524 .296935314 -.229166667 -.229166667 0 0 0 0
- .241750097 28.41330446 .001743458 .001863744 .280334669 0 -.066666667 0 0 0 0
- .568715734 28.55016737 .001909739 .00180085 .235855671 -.6875 -1.020833333 0 0 0 0
- .277937975 25.50437542 .731017524 .002991248 .113190236 .19 .2175 1 1 .19 .2175
- .339080392 25.64767893 .66450281 .002820632 .131544073 -1.37 -1.59 0 0 0 0
- .359300688 26.09686483 .623977776 .002109226 .174303672 .080833333 .071666667 1 1 .080833333 .071666667
- .283370007 26.41432923 .708728476 .001773511 .161593227 .950833333 .874166667 1 1 .950833333 .874166667
- .304940084 26.66027846 .677865035 .001596574 .15189254 -.0625 -.076666667 0 0 0 0
- .294818366 26.89539499 .695449267 .001461068 .135118249 -.229166667 -.229166667 0 0 0 0
- .284795203 26.9545516 .629483121 .001638164 .131756178 0 -.066666667 0 0 0 0
- .279372325 27.13884642 .61024393 .001651213 .13270035 -.6875 -1.020833333 0 0 0 0
- .110552041 18.93834643 .438427834 .148839373 -.265133047 .19 .2175 1 1 .19 .2175
- .10556194 19.25581373 .406414471 .100568207 .075440885 -1.37 -1.59 0 0 0 0
- .006153077 19.03163819 .366232364 .136663997 .153818952 .080833333 .071666667 1 1 .080833333 .071666667
- .005746742 19.09519144 .399934491 .150175252 .123390725 .950833333 .874166667 1 1 .950833333 .874166667
- .005374327 19.07775282 .496071174 .159170085 .080258426 -.0625 -.076666667 0 0 0 0
- .136276576 19.30507651 .507658377 .142603332 .079642567 -.229166667 -.229166667 0 0 0 0
- .1636692 19.63939214 .685610451 .109862015 .193835036 0 -.066666667 0 0 0 0
- .158684375 19.80365336 .720786521 .100330769 .056927355 -.6875 -1.020833333 0 0 0 0
- .430845587 22.50184589 .836454413 .003870149 .147014306 .19 .2175 1 1 .19 .2175
- .621551494 22.70730098 .746117118 .00325932 .181276485 -1.37 -1.59 0 0 0 0
- .463475749 22.87133545 .686717264 .003354758 .212279362 .080833333 .071666667 1 1 .080833333 .071666667
- .108240726 22.84216533 .750593838 .001095283 .167659658 .950833333 .874166667 1 1 .950833333 .874166667
- .253008476 22.95793929 .617524959 .001143871 .204653844 -.0625 -.076666667 0 0 0 0
- .382934075 23.02266185 .553463139 .001179692 .151383876 -.229166667 -.229166667 0 0 0 0
- .335620946 23.18739287 .580058171 .000800866 .220057134 0 -.066666667 0 0 0 0
- .467275957 23.26514371 .661974463 .000785864 .12007355 -.6875 -1.020833333 0 0 0 0
- .290121701 22.52889365 .530753791 .054134021 .093887549 .19 .2175 1 1 .19 .2175
- .356681711 22.73843678 .404121647 .046894063 .124975756 -1.37 -1.59 0 0 0 0
- .421016095 23.02831268 .502661377 .036874474 .137278413 .080833333 .071666667 1 1 .080833333 .071666667
- .408825027 23.19014679 .559231807 .030420459 .093582161 .950833333 .874166667 1 1 .950833333 .874166667
- .26640273 23.30130833 .511349071 .024533581 .076355876 -.0625 -.076666667 0 0 0 0
- .187387894 23.334203 .532592489 .030862728 .106894986 -.229166667 -.229166667 0 0 0 0
- .347631671 23.41284622 .529228409 .034205976 .109602497 0 -.066666667 0 0 0 0
- .317349041 23.61994727 .467313913 .035070541 .197328458 -.6875 -1.020833333 0 0 0 0
- .378630964 21.4703546 .595846511 .042316286 .015773034 .19 .2175 1 1 .19 .2175
- .284601827 21.76510245 .470171508 .034053216 .114665339 -1.37 -1.59 0 0 0 0
- .195023639 21.7925459 .567846305 .030892137 .176201045 .080833333 .071666667 1 1 .080833333 .071666667
- .006441567 21.97591689 .537204167 .0283743 .182782492 .950833333 .874166667 1 1 .950833333 .874166667
- .012680509 22.09716012 .602296338 .032650455 .201376666 -.0625 -.076666667 0 0 0 0
- .060546984 22.07736049 .561982668 .034085701 .185792679 -.229166667 -.229166667 0 0 0 0
- .07884402 22.07994809 .614824812 .036086808 .329140449 0 -.066666667 0 0 0 0
- .064499214 22.20026056 .582741923 .029513558 .145551581 -.6875 -1.020833333 0 0 0 0
- .19207338 23.06276717 .565638757 .131034069 .117057762 .19 .2175 1 1 .19 .2175
- .139492808 23.11325265 .772092633 .15215057 .173251022 -1.37 -1.59 0 0 0 0
- .285993138 23.24656106 .746585055 .16430575 .206220348 .080833333 .071666667 1 1 .080833333 .071666667
- .295923248 23.44990166 .648593105 .147348442 .161716802 .950833333 .874166667 1 1 .950833333 .874166667
- .270695932 23.38602642 .682607033 .178094801 .052872151 -.0625 -.076666667 0 0 0 0
- .214490495 23.43656005 .554326961 .137640849 .209627248 -.229166667 -.229166667 0 0 0 0
- .043121402 23.43907309 .677508792 .165039964 .121721117 0 -.066666667 0 0 0 0
- .223375393 23.46343468 .68110865 .202181333 .073710246 -.6875 -1.020833333 0 0 0 0
- .339718552 21.2344251 .767409001 .008206849 .139843237 .19 .2175 1 1 .19 .2175
- .235025896 21.27826053 .679094879 .009072152 .119855096 -1.37 -1.59 0 0 0 0
- .348921007 21.186846 .688456702 .011465957 .105573345 .080833333 .071666667 1 1 .080833333 .071666667
- .476506663 21.3095566 .765687395 .011156332 .161648187 .950833333 .874166667 1 1 .950833333 .874166667
- .191479285 21.42661214 .766090423 .01054857 .078742723 -.0625 -.076666667 0 0 0 0
- .466410504 21.36127859 .777319499 .01103361 .062430717 -.229166667 -.229166667 0 0 0 0
- .50568644 21.52828274 .793679258 .008487971 .122667811 0 -.066666667 0 0 0 0
- .466180499 21.4982303 .84363872 .009725173 .129256979 -.6875 -1.020833333 0 0 0 0
- .108454018 22.36298618 .444278324 .2440696 .493419733 .19 .2175 1 1 .19 .2175
- .109914372 22.43306905 .404253593 .248380834 .412256772 -1.37 -1.59 0 0 0 0
- .017831449 22.54816744 .431597341 .245524424 .474966084 .080833333 .071666667 1 1 .080833333 .071666667
- .040158106 22.60123793 .383102528 .252242783 .390938026 .950833333 .874166667 1 1 .950833333 .874166667
- .140050792 22.63740966 .401469791 .262220372 .345192053 -.0625 -.076666667 0 0 0 0
- .184041176 22.71749382 .387902262 .260107412 .360786721 -.229166667 -.229166667 0 0 0 0
- .176100663 22.65996573 .48140399 .299308232 .29355077 0 -.066666667 0 0 0 0
- .09245903 22.65678133 .467253022 .325555508 .348560644 -.6875 -1.020833333 0 0 0 0
- .350244039 20.6973908 .467876742 .12121218 .040291115 .19 .2175 1 1 .19 .2175
- .006753583 20.7957914 .501545721 .125373716 .031319042 -1.37 -1.59 0 0 0 0
- .079891526 20.65981615 .355779512 .171244304 .014193727 .080833333 .071666667 1 1 .080833333 .071666667
- .192718751 20.81271473 .266263336 .161805038 .011373148 .950833333 .874166667 1 1 .950833333 .874166667
- .121495606 20.92133356 .300294359 .151272759 .005844416 -.0625 -.076666667 0 0 0 0
- .011484247 21.01959554 .373773851 .13767265 .011053862 -.229166667 -.229166667 0 0 0 0
- .035411884 21.11124696 .372497086 .12680378 .013792555 0 -.066666667 0 0 0 0
- .012522381 21.1717046 .381019996 .112808938 .025663745 -.6875 -1.020833333 0 0 0 0
- .09139904 20.23639123 .393551882 .2047585 .002892159 .19 .2175 1 1 .19 .2175
- .338039749 20.23140445 .327805068 .221663523 .018775028 -1.37 -1.59 0 0 0 0
- .32595916 20.21169986 .330989504 .238973018 .010152255 .080833333 .071666667 1 1 .080833333 .071666667
- .040956692 20.16236244 .341386591 .267774628 .003100054 .950833333 .874166667 1 1 .950833333 .874166667
- .436870916 20.28462333 .316129507 .242866176 .002316064 -.0625 -.076666667 0 0 0 0
- .414670098 20.36801581 .303560044 .230601538 .006200523 -.229166667 -.229166667 0 0 0 0
- .487233032 20.50799717 .23694937 .208842049 .018422447 0 -.066666667 0 0 0 0
- .015654702 20.87913041 .130691446 .148738051 .14071213 -.6875 -1.020833333 0 0 0 0
- .105392358 21.08873329 .652210085 .034136824 .059847596 .19 .2175 1 1 .19 .2175
- .112824662 21.14456294 .669938453 .042686111 .057176866 -1.37 -1.59 0 0 0 0
- .123512132 21.61384445 .533598955 .032003319 .05279703 .080833333 .071666667 1 1 .080833333 .071666667
- .039310127 21.78651888 .6228215 .027579038 .062185047 .950833333 .874166667 1 1 .950833333 .874166667
- .032309881 21.92526584 .661623918 .031448719 .038247784 -.0625 -.076666667 0 0 0 0
- .270640611 21.99266187 .682898254 .034826732 .041928515 -.229166667 -.229166667 0 0 0 0
- .247664673 22.02012269 .671861836 .039046186 .044568905 0 -.066666667 0 0 0 0
- .175690903 22.16940099 .578022503 .039344008 .038594595 -.6875 -1.020833333 0 0 0 0
- .281761161 24.10374531 .455886121 .309870163 .109358193 .19 .2175 1 1 .19 .2175
- .226559382 24.13275669 .47774308 .335923938 .203498896 -1.37 -1.59 0 0 0 0
- .269968665 24.09297115 .512578694 .388736199 .12964095 .080833333 .071666667 1 1 .080833333 .071666667
- .241952646 24.17354171 .492644229 .403227582 .082216368 .950833333 .874166667 1 1 .950833333 .874166667
- end
------------------ copy up to and including the previous line ------------------
Listed 100 out of 6072 observations
Use the count() option to list more
.