Hello all,
I am using dynamic panel estimators xtabond and xtdpsys in Stata 18.
I would like to know why the number of observations is different when I use the above two estimators. The codes are as follows
.
I was under the impression that xtdp is an extension of xtabond and is a generalized function that allows for autocorrelated errors, therefore I am not sure why the number of observations get changed. I will really appreciate if anyone shares their thoughts or suggests some useful resources.
I am using dynamic panel estimators xtabond and xtdpsys in Stata 18.
I would like to know why the number of observations is different when I use the above two estimators. The codes are as follows
Code:
xi: xtdpdsys f.delta_alt_w1 i.year ln_totalasset_w score_avg_state_alt_w ROA_w growth_w boardsize_w ESGValueScore_w political_proposal_fy_w score_avg_3N_alt_w capital_intensity_w ln_pol_contribution_fy_w tnic3hhi_w CPA_outperform_3N_alt_w1 CPA_short_3N_alt_w1, vce(r)
HTML Code:
i.year _Iyear_2008-2021 (naturally coded; _Iyear_2008 omitted)
note: _Iyear_2009 omitted from div() because of collinearity.
note: _Iyear_2010 omitted from div() because of collinearity.
note: _Iyear_2019 omitted from div() because of collinearity.
note: _Iyear_2020 omitted from div() because of collinearity.
note: _Iyear_2021 omitted from div() because of collinearity.
note: _Iyear_2009 omitted because of collinearity.
note: _Iyear_2010 omitted because of collinearity.
note: _Iyear_2011 omitted because of collinearity.
note: _Iyear_2012 omitted because of collinearity.
note: _Iyear_2020 omitted because of collinearity.
note: _Iyear_2021 omitted because of collinearity.
System dynamic panel-data estimation Number of obs = 1,839
Group variable: f_id Number of groups = 401
Time variable: year
Obs per group:
min = 1
avg = 4.586035
max = 8
Number of instruments = 56 Wald chi2(21) = 165.44
Prob > chi2 = 0.0000
One-step results
------------------------------------------------------------------------------------------
| Robust
F.delta_alt_w1 | Coefficient std. err. z P>|z| [95% conf. interval]
-------------------------+----------------------------------------------------------------
delta_alt_w1 | -.0252575 .0210547 -1.20 0.230 -.0665239 .0160088
_Iyear_2013 | .904637 .2434117 3.72 0.000 .4275588 1.381715
_Iyear_2014 | 1.023469 .2685259 3.81 0.000 .4971677 1.54977
_Iyear_2015 | 1.648656 .4327655 3.81 0.000 .8004515 2.496861
_Iyear_2016 | 1.716354 .4211236 4.08 0.000 .8909666 2.541741
_Iyear_2017 | 1.999446 .4554555 4.39 0.000 1.106769 2.892122
_Iyear_2018 | 2.174236 .4795256 4.53 0.000 1.234383 3.114089
_Iyear_2019 | 2.902416 .5566939 5.21 0.000 1.811316 3.993516
ln_totalasset_w | .1034448 .4016108 0.26 0.797 -.6836979 .8905875
score_avg_state_alt_w | -.0236885 .0185764 -1.28 0.202 -.0600976 .0127206
ROA_w | 2.956396 1.306307 2.26 0.024 .3960812 5.51671
growth_w | 9.20e-07 8.33e-06 0.11 0.912 -.0000154 .0000172
boardsize_w | -.050731 .076449 -0.66 0.507 -.2005683 .0991063
ESGValueScore_w | -1.818897 1.326591 -1.37 0.170 -4.418968 .7811737
political_proposal_fy_w | .0889893 .1260127 0.71 0.480 -.1579911 .3359696
score_avg_3N_alt_w | -.1462523 .0194679 -7.51 0.000 -.1844086 -.108096
capital_intensity_w | 3.4919 2.835968 1.23 0.218 -2.066495 9.050295
ln_pol_contribution_fy_w | .0133927 .0106158 1.26 0.207 -.007414 .0341994
tnic3hhi_w | -1.317324 .4846862 -2.72 0.007 -2.267292 -.3673568
CPA_outperform_3N_alt_w1 | -.1134739 .0159907 -7.10 0.000 -.1448151 -.0821327
CPA_short_3N_alt_w1 | .1712967 .0208966 8.20 0.000 .1303401 .2122532
_cons | 3.746604 4.296416 0.87 0.383 -4.674217 12.16742
------------------------------------------------------------------------------------------
Instruments for differenced equation
GMM-type: L(2/.).F.delta_alt_w1
Standard: D._Iyear_2011 D._Iyear_2012 D._Iyear_2013 D._Iyear_2014 D._Iyear_2015
D._Iyear_2016 D._Iyear_2017 D._Iyear_2018 D.ln_totalasset_w
D.score_avg_state_alt_w D.ROA_w D.growth_w D.boardsize_w
D.ESGValueScore_w D.political_proposal_fy_w D.score_avg_3N_alt_w
D.capital_intensity_w D.ln_pol_contribution_fy_w D.tnic3hhi_w
D.CPA_outperform_3N_alt_w1 D.CPA_short_3N_alt_w1
Instruments for level equation
GMM-type: D.delta_alt_w1
Standard: _cons
.
end of do-file
Code:
xi: xtabond f.delta_alt_w1 i.year ln_totalasset_w score_avg_state_alt_w ROA_w growth_w boardsize_w ESGValueScore_w political_proposal_fy_w score_avg_3N_alt_w capital_intensity_w ln_pol_contribution_fy_w tnic3hhi_w CPA_outperform_3N_alt_w1 CPA_short_3N_alt_w1, vce(r)
HTML Code:
i.year _Iyear_2008-2021 (naturally coded; _Iyear_2008 omitted)
note: _Iyear_2009 omitted from div() because of collinearity.
note: _Iyear_2010 omitted from div() because of collinearity.
note: _Iyear_2019 omitted from div() because of collinearity.
note: _Iyear_2020 omitted from div() because of collinearity.
note: _Iyear_2021 omitted from div() because of collinearity.
note: _Iyear_2009 omitted because of collinearity.
note: _Iyear_2010 omitted because of collinearity.
note: _Iyear_2011 omitted because of collinearity.
note: _Iyear_2012 omitted because of collinearity.
note: _Iyear_2020 omitted because of collinearity.
note: _Iyear_2021 omitted because of collinearity.
Arellano–Bond dynamic panel-data estimation Number of obs = 1,434
Group variable: f_id Number of groups = 362
Time variable: year
Obs per group:
min = 1
avg = 3.961326
max = 7
Number of instruments = 49 Wald chi2(21) = 141.96
Prob > chi2 = 0.0000
One-step results
(Std. err. adjusted for clustering on f_id)
------------------------------------------------------------------------------------------
| Robust
F.delta_alt_w1 | Coefficient std. err. z P>|z| [95% conf. interval]
-------------------------+----------------------------------------------------------------
delta_alt_w1 | -.0159495 .0175571 -0.91 0.364 -.0503608 .0184619
_Iyear_2013 | .9016302 .2315005 3.89 0.000 .4478977 1.355363
_Iyear_2014 | 1.004152 .255041 3.94 0.000 .5042813 1.504024
_Iyear_2015 | 1.651665 .4274178 3.86 0.000 .8139415 2.489389
_Iyear_2016 | 1.715784 .4129452 4.15 0.000 .9064258 2.525141
_Iyear_2017 | 1.964593 .4396862 4.47 0.000 1.102824 2.826362
_Iyear_2018 | 2.169019 .4722926 4.59 0.000 1.243343 3.094696
_Iyear_2019 | 2.8611 .5401609 5.30 0.000 1.802404 3.919796
ln_totalasset_w | .4475541 .3441132 1.30 0.193 -.2268954 1.122004
score_avg_state_alt_w | -.0228492 .0177869 -1.28 0.199 -.0577108 .0120124
ROA_w | 2.833357 1.368295 2.07 0.038 .1515482 5.515166
growth_w | -1.09e-06 8.29e-06 -0.13 0.895 -.0000173 .0000152
boardsize_w | -.0673055 .0781641 -0.86 0.389 -.2205044 .0858934
ESGValueScore_w | -1.360737 1.275509 -1.07 0.286 -3.860689 1.139215
political_proposal_fy_w | .1034184 .1291833 0.80 0.423 -.1497763 .356613
score_avg_3N_alt_w | -.1529579 .0200951 -7.61 0.000 -.1923436 -.1135722
capital_intensity_w | 3.890721 3.833003 1.02 0.310 -3.621827 11.40327
ln_pol_contribution_fy_w | .0170141 .0085031 2.00 0.045 .0003484 .0336799
tnic3hhi_w | -1.134279 .4437712 -2.56 0.011 -2.004054 -.2645029
CPA_outperform_3N_alt_w1 | -.1188579 .0167534 -7.09 0.000 -.1516938 -.0860219
CPA_short_3N_alt_w1 | .1760453 .0216054 8.15 0.000 .1336994 .2183912
_cons | .1569741 3.861243 0.04 0.968 -7.410923 7.724871
------------------------------------------------------------------------------------------
Instruments for differenced equation
GMM-type: L(2/.).F.delta_alt_w1
Standard: D._Iyear_2011 D._Iyear_2012 D._Iyear_2013 D._Iyear_2014 D._Iyear_2015
D._Iyear_2016 D._Iyear_2017 D._Iyear_2018 D.ln_totalasset_w
D.score_avg_state_alt_w D.ROA_w D.growth_w D.boardsize_w
D.ESGValueScore_w D.political_proposal_fy_w D.score_avg_3N_alt_w
D.capital_intensity_w D.ln_pol_contribution_fy_w D.tnic3hhi_w
D.CPA_outperform_3N_alt_w1 D.CPA_short_3N_alt_w1
Instruments for level equation
Standard: _cons
.
end of do-file
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float delta_alt_w1 double year float(ln_totalasset_w ROA_w growth_w) int boardsize_w double ESGValueScore_w float(political_proposal_fy_w ln_pol_contribution_fy_w) double tnic3hhi_w float(CPA_outperform_3N_alt_w1 CPA_short_3N_alt_w1)
. 2015 7.749114 . . . . 0 0 . 12.333333 0
.03125 2016 9.751296 -.031239634 3012.611 . . 0 0 . 9.75 0
. 2017 9.74184 .020136975 1302.891 . . 0 0 . . .
. 2018 9.753165 -.03539827 266.337 12 .1555077710513583 0 0 .3838179222 . .
. 2019 9.685558 -.0263715 543.818 11 .1941982272998987 0 0 .3302832737 . .
. 2020 9.687626 -.03922538 189.13 11 .24982679330691193 0 0 .5605535151 . .
. 2021 9.734735 -.02017361 -7.676 12 .36695518788930726 0 0 .4895962289 . .
. 2012 11.78367 .021862175 3193 14 .6102775498617466 1 0 .1142957992 24.666666 0
-.029411765 2013 11.70608 .02603312 -1425 14 .5968221269136472 0 0 .0460800989 12.333333 0
0 2014 11.693303 .02463951 -1211 12 .5584765325011157 0 0 .0478295262 14 0
0 2015 11.680945 .02141239 -1824 13 .5296724349422801 0 0 .062697926 9 0
0 2016 11.773896 .020482363 1470 13 .5154328983362487 0 0 .0962821763 15.833333 0
0 2017 11.82932 .033552695 -674 15 .6048361022853835 0 0 .0408819999 13 0
.03030303 2018 11.852294 .02079683 68 12 .5492936938663266 0 0 .0532723242 16.166666 0
-.029411765 2019 11.936676 .021627566 601 11 .5926075220805899 0 0 .1385639629 9.666667 0
0 2020 12.014222 .02894249 346 11 .530323647633256 0 0 .1485800449 8.666667 0
. 2021 11.967447 .027452996 -1678 11 .5390995917719975 0 0 .0718635537 . .
. 2012 10.64137 -.021802533 772 11 .4820770921921533 0 0 .0427758323 . .
. 2013 10.606857 .002821014 -2155 12 .4625525629917007 0 0 .0337432054 . .
. 2014 10.570445 .019735154 1255 12 .5385435225369882 0 0 .0500065232 . .
. 2015 10.51461 .008303935 -2280 10 .5921660900917453 0 0 .0358916575 . .
4.4 2016 10.494575 -.031285472 -1280 10 .6465284848454079 0 0 .0310609873 . .
.05555556 2017 10.40765 -.035062816 -3056 10 .6062257909636658 0 0 .0402249633 . .
0 2018 10.389642 .03699148 206 10 .6540216299159198 0 0 .0441972487 . .
0 2019 10.42371 .009004992 -547 10 .6915820998610493 0 0 .0515756886 . .
0 2020 10.451695 .0013293645 -529 10 .7230116624628844 0 12.911645 .036256181 . .
. 2021 10.40314 -.012407851 1481 11 .7415453921777238 0 0 .0571510106 . .
0 2012 12.514715 .02667499 711 12 .6664318863711056 2 0 .1631564392 0 3.5
1.2380953 2013 12.53461 .06569422 1318 14 .6654143751358915 2 15.40273 .1877636515 20 0
-.04255319 2014 12.587344 .021254726 3695 15 .6373118384330541 2 15.77629 .1912309825 16 0
.15555556 2015 12.905878 .033141118 14354 15 .6459996691769385 2 15.168892 .199908462 16.333334 0
.03846154 2016 12.908727 .03213305 16962 14 .6049431326501389 2 15.77629 .1914303256 17.666666 0
-.11111111 2017 13.003798 .06631434 -2974 13 .6258264757650418 2 15.77629 .2037224948 2 0
.125 2018 13.184143 .036419086 10016 13 .6349010292370355 1 15.77629 .1831834946 7.5 0
.2777778 2019 13.220703 .025201706 10460 14 .6866705817628004 1 15.69479 .1279349315 17.5 0
0 2020 13.172602 -.009844777 -9505 14 .7379864394349797 0 15.671705 .1643480534 29.333334 0
. 2021 13.220618 .03640355 -2896 13 .7214643674656367 2 0 .1742764737 . .
.8461539 2012 11.11595 .0886878 1022.651 12 .8057128481136637 1 13.313687 .1307484903 22.166666 0
.08333334 2013 10.667862 .05997253 -11547 11 .7711674257147759 1 12.634687 .3393328724 17.333334 0
.03846154 2014 10.628013 .05533616 -1601 11 .7354207368789126 1 13.087877 .2485701272 13.666667 0
0 2015 10.627334 .10723204 158 11 .8015516760391339 0 12.052636 .2304300794 21.5 0
0 2016 10.871725 .026582615 448 12 .7934518925750189 0 12.3417 .2258542081 13.916667 0
.037037037 2017 11.241773 .006255738 6537 12 .8189123159637816 0 11.943857 .4371972373 13.636364 0
0 2018 11.115026 .03525226 3188 13 .7760097421335387 0 12.059884 .2550446595 22.46154 0
.017857144 2019 11.1256 .05431084 1326 15 .8040399444762671 0 12.11611 .2338781285 23.333334 0
0 2020 11.192003 .06195898 2704 15 .8178891200004429 1 12.183556 .2708156984 22.294117 0
. 2021 11.227854 .09403425 8467 13 .8081530413591038 1 0 .3005667109 . .
. 2012 10.203888 .1953125 936.049 . . 0 0 . . .
. 2013 10.281856 .14137955 410 9 .4517397286759887 0 10.9768 .0862772424 . .
. 2014 10.22365 .06439903 1170 9 .6133276240575489 0 12.33579 .083052535 15.666667 0
0 2015 10.87899 .09696513 2899 9 .6786291678714086 0 15.316918 .0843889553 21.9375 0
0 2016 11.098908 .09006187 2779 9 .7511602529817795 1 14.850392 .0930728195 22.05882 0
.015873017 2017 11.167417 .0750007 2578 10 .7991271179965028 1 13.004032 .090408 26 0
0 2018 10.991241 .09581817 4537 11 .7541276864659012 1 12.941084 .0965050575 22.31579 0
0 2019 11.397683 .08844751 513 11 .7823523652426244 1 12.721889 .1040870363 19.61111 0
0 2020 11.92215 .030657856 12518 12 .8064472766716309 1 12.79719 .0634100897 19.235294 0
. 2021 11.894979 .0787694 10413 13 .8027819504415102 1 0 .0730326821 . .
. 2012 6.169849 .08831817 31.749 . . 0 0 .0876544396 . .
. 2013 6.169849 .035787486 25.519 . . 0 0 .086133981 . .
. 2014 6.169849 .2717677 46.668 . . 0 0 .0796202904 . .
. 2015 6.169849 .08998398 99.232 8 . 0 0 .0632431096 . .
. 2016 6.310671 .0946851 115.761 9 .21013227080699623 0 0 .068539994 . .
. 2017 6.667434 .14264187 148.445 8 .2613484181292542 0 0 .0588474439 . .
. 2018 6.960676 .2456651 175.683 7 .20354418439977875 0 0 .0619311623 . .
. 2019 7.103702 .1668848 71.451 7 .20335330166377644 0 0 .0749990967 0 23.6
0 2020 7.309453 .15091754 6.639 7 .23317581712809135 0 0 .1238038972 0 24.294117
. 2012 9.560997 .0809155 101 12 .15122554552433962 0 0 .1776840981 . .
. 2013 9.547669 .072081074 -273 7 .40640506467998117 0 0 .4194478905 . .
. 2014 9.598727 .05662553 -175 8 .42013154935051267 0 0 .3516205646 . .
. 2015 9.632401 .05848797 256 9 .5745485183483238 0 0 .2129662255 . .
. 2016 9.76721 .05535182 1944 9 .5281378121209034 0 0 .3602605789 0 24.26923
.2 2017 9.834566 .014623956 409 9 .44211096986772486 0 0 .3506583375 0 21.22222
-.16666667 2018 9.788918 .10165405 483 10 .52473471122077 0 0 .3064910917 0 21.75
0 2019 9.895707 .07573696 -1011 10 .5985821767595451 0 0 .7013642662 0 21.03226
-.75 2020 10.047977 .09507118 1597 10 .6729393420878621 1 0 .4238736489 0 28.22857
. 2021 10.12887 .10771871 717 10 .7090544115781713 1 0 .7827431244 . .
. 2012 7.459857 .066958375 138 . . 0 0 .5308039868 . .
. 2013 7.551607 .06691879 155.4 . . 0 0 .5128052248 . .
. 2014 7.681606 .08108482 304.4 10 .1654493005638838 0 0 .4982653225 . .
. 2015 7.795482 .09141423 313.2 11 .311196019748818 0 0 .4995656502 . .
. 2016 7.988882 .09864315 584.6 10 .3053531629795233 0 0 .465305053 . .
. 2017 7.972328 .11094633 213.8 11 .4182826307717656 0 0 .5858438553 . .
-.75 2018 8.002627 .11697003 175 11 .411689457078758 0 0 1 . .
. 2019 8.062243 .10414828 -7.4 10 .6158972947761268 0 0 .9654010416 . .
. 2020 8.158144 .07111149 -346.4 12 .5818434980089872 0 0 .8458912314 . .
. 2021 8.181748 .08567593 134.7 10 .6293355822741981 0 0 1 . .
. 2012 9.207789 .0834902 187.419 12 .6860462520636819 0 0 .0738893447 . .
. 2013 9.247664 .027936095 -348.437 13 .7405852020142666 0 13.910822 .1000704907 . .
. 2014 9.285989 .02488404 91.825 13 .7026174808711698 0 13.997833 .0917740198 0 4
.10714286 2015 9.369604 .05368631 648.446 13 .7506520244338267 0 13.910822 .1046419655 7.375 0
.1935484 2016 9.449917 .09197856 1058.919 10 .7861359508264556 0 14.077875 .144173851 8.961538 0
0 2017 9.584353 .11653864 1447.075 10 .7858290850267025 0 13.910822 .1282191388 10.925926 0
.13513513 2018 9.839945 .13803707 1728.503 11 .785571053411965 0 13.73213 .0874815865 16.571428 0
-.023809524 2019 9.940899 .142154 2141.289 11 .6560987821223934 0 14.10818 .1157108881 16.129032 0
0 2020 10.097573 .2166035 1696.703 11 .7454801962481726 0 14.054528 .1086949367 12.914286 0
. 2021 10.21248 .1770126 2917 10 .7463923622090778 1 0 .1271563833 . .
. 2012 8.4368105 .08402376 34.541 10 .4388238456772243 0 0 .0713739086 . .
. 2013 8.624211 .07039963 288.811 9 .4554833238653906 0 0 .0663247445 . .
. 2014 8.98248 .06201994 3350.047 10 .38746859581930465 0 0 .1206197452 . .
. 2015 9.003878 .05819586 -106.843 14 .30015848425023506 0 0 .0560887612 . .
. 2016 9.025821 .05527603 -169.339 11 .33369833852679837 0 0 .1198861903 0 17
6 2017 9.045737 .05605849 -193.895 11 .5502233839686952 0 0 .2950055673 0 11.5
-.75 2018 9.109487 .04688237 206.77 10 .6294117470549816 0 0 .2030133361 0 17.5
14 2019 9.327992 .04328532 128.449 11 .43305700805818564 0 0 .273813859 0 3
0 2020 9.379209 .04164157 397.318 11 .5591737059061923 0 0 .2446405442 0 3.5
. 2021 9.408716 .05052464 891.668 9 .6278120240281531 0 0 .2491511255 . .
. 2012 8.294049 -.21671093 -1146 12 .6578137132076772 0 8.160804 .1744318645 . .
. 2013 8.374938 -.01913765 -123 10 .6592924953827164 0 7.601402 .1512262546 . .
. 2014 8.234035 -.10698168 207 12 .6370966454846023 0 7.824446 .1316588995 . .
. 2015 8.042056 -.2122869 -1515 9 .8012715240613594 0 7.313887 .1606274159 . .
. 2016 8.108021 -.1496537 281 9 .7172747584726359 0 0 .1455634861 . .
. 2017 8.171882 .012146893 1057 10 .7077292009648519 0 0 .161274949 0 8
0 2018 8.4242 .073968396 1146 9 .6850730945852896 0 6.216606 .1206272111 0 7
-.5714286 2019 8.70417 .05656934 256 8 .6626197792118176 0 0 .1201150964 0 16.5625
0 2020 9.100749 .2717677 3032 8 .6613324018203032 0 10.328788 .1042720503 0 16.777779
. 2021 9.426983 .25460988 6671 8 .7130802864622029 0 0 .1166049562 . .
. 2012 10.633316 .03995469 2816.1 12 .574455724820841 0 14.564932 .1003090547 4.6666665 0
.018867925 2013 10.81721 .03837038 10689 12 .594537269211212 0 14.416787 .0950097358 0 3.666667
0 2014 10.885606 .03821573 10718.3 13 .6687265767532701 0 14.73064 .1060737861 0 1
0 2015 10.886017 .04474011 2223.7 13 .64524253011939 0 14.601192 .0978144722 0 5.4
.0925926 2016 11.143975 .03284355 2928.1 12 .6381331150438662 0 14.640782 .1391009468 7.666667 0
0 2017 10.91783 .034523398 -2708 12 .6575046070856906 0 14.508409 .1327386894 5 0
. 2012 8.730222 .02812303 100.7 10 .30568513707028655 0 0 .2145074142 . .
. 2013 8.751285 .05705197 383.3 10 .2953224164201151 0 0 .0813643289 . .
. 2014 8.948729 .05872878 322.1 11 .3794336185045618 0 0 .1160561643 . .
. 2015 8.9599285 .06628301 -26.4 9 .3804728934935791 0 0 .1050281977 0 22.125
1 2016 9.076706 .05403984 -289.9 9 .4060185692801264 0 0 .1672667904 0 30.625
-.5 2017 9.07132 .07923375 110.4 9 .4346663709019733 0 0 .0814620539 0 37.285713
0 2018 9.014216 .02963828 73.4 11 .5354129201728836 0 0 .1602151693 0 40
0 2019 8.942919 .002051349 -138.8 11 .5177113790915282 0 0 .0976513347 0 51
. 2020 8.973212 .02563095 -212.1 6 .5449539176735618 0 0 .0533764779 . .
. 2021 9.091151 .06373079 384.9 8 .6012299326133204 0 0 .0635488156 . .
. 2012 9.262553 .10943432 232 9 .7717908305368468 0 0 .1297670987 . .
. 2013 9.27669 .0677522 -76 9 .769378832027809 0 0 .1214670204 . .
. 2014 9.290168 .0465331 199 9 .8297878483007792 0 0 .1572050401 0 12
0 2015 8.919854 .05361679 -2943 11 .7809494088880002 0 0 .1652562325 0 15.142858
.1818182 2016 8.962135 .05921559 164 10 .884762239735611 0 0 .1900307842 0 10.625
0 2017 9.039078 .0811773 270 11 .8763995996934478 0 0 .2426955132 0 13.7
-.4615385 2018 9.052633 .03699801 442 10 .8876690513988897 0 0 .2216868346 0 15.7
0 2019 9.153981 .11330935 249 11 .8788510366829416 0 0 .2731669679 0 15.181818
0 2020 9.172327 .07468578 176 11 .8663586187976943 0 0 .2008298759 0 17.5
. 2021 9.278466 .1130313 980 11 .8778561893663059 0 0 .2449295187 . .
. 2012 9.737539 .06890059 -470.3 11 .8442895640271936 0 11.453716 .2715728872 14 0
.11764706 2013 9.789764 .05569717 568.7 15 .7550968950206038 0 0 .2612319348 0 13
.07017544 2014 9.785779 .05577898 258.6 11 .7879753149327128 0 11.566476 .205012715 0 9
.032786883 2015 9.766413 .07328206 -544.1 8 .7939736120241484 0 11.635152 .17825809 0 10
-.06349207 2016 9.801194 .034953725 -370.5 8 .7312856908157517 0 12.0131 .263572127 0 14
-.6440678 2017 9.823751 .16247185 -1336.8 8 .7926088076829463 0 11.248322 .5977855545 0 33
-.04761905 2018 9.861535 .07809868 742.6 8 .8530798050400724 0 11.258046 .0999403584 0 34.5
0 2019 9.849179 .09291129 -11.3 8 .7993179460218472 0 0 .1853595218 0 36.5
end
I was under the impression that xtdp is an extension of xtabond and is a generalized function that allows for autocorrelated errors, therefore I am not sure why the number of observations get changed. I will really appreciate if anyone shares their thoughts or suggests some useful resources.
