panel group FMOLS
Re: panel group FMOLS
What's special about the cross section that doesn't seem to work? Missing data?
Re: panel group FMOLS
Dear Tom,
Surprisingly that's not the problem because all my cross sections have exactly the same data.Indeed it is a balanced panel.
Regards.
Surprisingly that's not the problem because all my cross sections have exactly the same data.Indeed it is a balanced panel.
Regards.
Re: panel group FMOLS
Dear Tom,
Can we retrieve p-values for individual coefficients?
Thanks.
Regards.
Can we retrieve p-values for individual coefficients?
Thanks.
Regards.
Re: panel group FMOLS
You can retrieve the individual t-statistics (%%ITSTATS) , so you have the information needed.sanjeev wrote:Dear Tom,
Can we retrieve p-values for individual coefficients?
Thanks.
Regards.
Re: panel group FMOLS
You would have to post the program and data and what you did with EViews. Did you ask the people at EViews about this?sanjeev wrote:Dear Tom,
Surprisingly that's not the problem because all my cross sections have exactly the same data.Indeed it is a balanced panel.
Regards.
Re: panel group FMOLS
Yes,I did ask them.They replied that since group-mean FMOLS requires estimation of long-run variance which is a tedious calculation,so differences in settings may lead to substantive differences in coefficients.
Regards.
Regards.
Re: panel group FMOLS
Dear Tom,
My dependent variable is Y while independent variables are X1,X2...................,X6.
The following is the program I used in Eviews:
Dependent Variable: Y
Method: Panel Fully Modified Least Squares (FMOLS)
Date: 03/15/16 Time: 22:01
Sample: 1992 2013
Periods included: 22
Cross-sections included: 8
Total panel (balanced) observations: 176
Panel method: Grouped estimation
Cointegrating equation deterministics: C
Long-run covariance estimates (Bartlett kernel, Newey-West fixed
bandwidth)
My data is as follows:
Please help me out!
Thanks,
Regards.
My dependent variable is Y while independent variables are X1,X2...................,X6.
The following is the program I used in Eviews:
Dependent Variable: Y
Method: Panel Fully Modified Least Squares (FMOLS)
Date: 03/15/16 Time: 22:01
Sample: 1992 2013
Periods included: 22
Cross-sections included: 8
Total panel (balanced) observations: 176
Panel method: Grouped estimation
Cointegrating equation deterministics: C
Long-run covariance estimates (Bartlett kernel, Newey-West fixed
bandwidth)
My data is as follows:
Code: Select all
Y X1 X2 X3 X4 X5 X6
1 - 92 0.861862698 2.849314083 9.212537956 3.772468022 3.617984952 10.49306345 3.064664802
1 - 93 0.982476409 2.699787992 9.399637543000001 3.836248476 3.789740682 11.06004511 2.964632353
1 - 94 1.095746818 3.068339491 9.322865163 3.864868922 3.737083195 11.21385883 2.972716456
1 - 95 1.191048945 3.014035904 9.211439767 3.930069582 3.729675929 11.52384562 2.978742643
1 - 96 1.272756078 2.8662841 9.361171261999999 3.998400682 3.694313002 11.76032446 2.964752499
1 - 97 1.348474252 2.949006904 9.447150114 4.03072917 3.630430496 11.94467541 2.888081294
1 - 98 1.412444209 2.886734957 9.528866828 4.047624773 3.60795977 12.07343228 2.846141748
1 - 99 1.475203809 2.885803809 9.656691473 4.064520376 3.598313915 12.13451757 2.780737533
1 - 00 1.546499661 3.029199092 10.14037621 4.060923068 3.553086022 12.17224695 2.691188434
1 - 01 1.616390851 2.994385584 10.31021853 4.062859743 3.585332427 12.22166052 2.643152095
1 - 02 1.696789253 3.103268484 10.59177293 4.067740457 3.62850299 12.28535968 2.593670402
1 - 03 1.786077566 3.279856776 10.94674568 4.097398228 3.713063181 12.33872678 2.519719321
1 - 04 1.874940008 3.419467552 11.09416233 4.133353369 3.762085583 12.4109178 2.565376589
1 - 05 1.977325668 3.517517238 11.44555627 4.169308509999999 3.734855431 12.51390287 2.462054392
1 - 06 2.092356016 3.573793248 11.71437949 4.20526365 3.754357191 12.58642163 2.371262739
1 - 07 2.220528765 3.553429623 11.93858528 4.266272711 3.723129585 12.69798147 2.338535143
1 - 08 2.309188230000001 3.456163822 12.17859353 4.322526200999999 3.776660186 12.84286903 2.335863355
1 - 09 2.394018666 3.166884965 12.34189641 4.371678527 3.862490644 13.06702613 2.290683371
1 - 10 2.491408873 3.264734518 12.58815312 4.420390850999999 3.857506598 13.28417095 2.26430509
1 - 11 2.577891254 3.236968654 12.9380294 4.461440683 3.853684631 13.47555506 2.254798611
1 - 12 2.648828143 3.187137381 13.1906069 4.488389148000001 3.857036755 13.63264725 2.254263261999999
1 - 13 2.719304387 3.149351181 13.4658623 4.526225675 3.864471968 13.77134235 2.241576758
2 - 92 1.202421087 2.162232986 7.129297549 3.770227559 3.188101377 7.592774517 3.358138487
2 - 93 1.234475345 2.268482945 7.097548851 3.815911365 3.058066422 7.830350098 3.356094412
2 - 94 1.275936259 2.273945552 7.370230642 3.826009458 3.143890822 8.157602572 3.341871716
2 - 95 1.319632095 2.366224048 7.342779189 3.824494549 3.260145885 8.637782951 3.267909941
2 - 96 1.375168175 2.322993821 7.41517511 3.829061308 3.093647958 9.007711204 3.300545097
2 - 97 1.387600892 2.351990889 7.563200592 3.846021655 3.199214377 9.181884322 3.253741234
2 - 98 1.427024615 2.382187945 7.717351272 3.81773749 3.157595528 9.478601312 3.25002485
2 - 99 1.473975464 2.420785093 7.6989362 3.788629988 3.289292512 9.619253197000001 3.198766096
2 - 00 1.488327335 2.547351807 7.6989362 3.830670641 3.182823555 9.701307221 3.136443886
2 - 01 1.487311802 2.513003207 7.77443551 3.835738543 3.241529874 9.887151016000003 3.132110506
2 - 02 1.475305361 2.64038086 7.898411093 3.875540332 3.217606403 10.15914767 3.029927953
2 - 03 1.508444079 2.687186812999999 8.138856751 3.926833782 3.263396767 10.39050789 3.032197015
2 - 04 1.52881648 2.865129644 8.297543529 3.960688781 3.479828006 10.54692547 2.945940606
2 - 05 1.598763414 2.959076009 8.459775921 4.009835144 3.534551456 10.67363224 2.934417533
2 - 06 1.689356341 3.04782532 8.645762292 4.027147754999999 3.579948334 11.16860649 2.906257415
2 - 07 1.773415838 3.017048183 8.74766979 4.071675210000001 3.638485377 11.56921587 2.904508687
2 - 08 1.837504143 3.161299476 8.767951910000001 4.126020215999999 3.570248048 11.7377608 2.878317866
2 - 09 1.915838517 2.998210363 8.890410552 4.115759941 3.591734091 12.05069229 2.875632358
2 - 10 1.995331632 3.089691361 9.088511664 4.175495271 3.598090771 12.23359137 2.901719869
2 - 11 2.053735191 3.189428451 9.087155271 4.226976509 3.661920031 12.23734757 2.910757614
2 - 12 2.096845105 3.195609726 9.16461052 4.26928002 3.591746114 12.32380009 2.892355455
2 - 13 2.14325614 3.22535947 9.275097619 4.248351945999999 3.481888466 12.33072784 2.887858828
3 - 92 1.712417632 3.328319711 4.204692619 3.801379671 3.417001415 9.393782134 2.927351633
3 - 93 1.764370729 3.286713462 3.63758616 3.793667306 3.383658584 9.547986919 2.88367312
3 - 94 1.803483982 3.277575871 3.36729583 3.816697306 3.435833671 9.693226292 2.849912067
3 - 95 1.908877637 3.270031364 4.110873864 3.877827962 3.463487724 9.934329512 2.841318869
3 - 96 1.916949079 3.251325682 3.688879454 3.934068214 3.423985692 10.19881966 2.813753877
3 - 97 1.947006486 3.327165507 4.369447852000001 4.00523371 3.457932465 10.36092664 2.778329799
3 - 98 1.800870763 3.969690513 4.532599493 4.009304695 2.819887524 10.35435455 2.894964549
3 - 99 1.79725283 3.569930609 5.023880521 4.010305672000001 2.430749709 10.29402344 2.976175784
3 - 00 1.834137577 3.713018463 5.056245804999999 4.022107147 3.102148594 10.12904619 2.747397267
3 - 01 1.858485102 3.664385241 5.356586275 4.030042635 3.115258958 9.629266473 2.727216365
3 - 02 1.892866387 3.486996449 5.455321115 4.062731941 3.063581101 8.868306297 2.738026504
3 - 03 1.948089302 3.416993816 5.303304908000001 4.117297125000001 3.242533713 9.242633296999998 2.72033105
3 - 04 1.964077201 3.47248474 5.424950016999999 4.1486607 3.180399643 9.671420554999999 2.662758499000001
3 - 05 2.015575344000001 3.52833703 5.459585513999999 4.122929266 3.222126932 10.62587795 2.574642126
3 - 06 2.052147773000001 3.435106459 5.66296048 4.164761397000001 3.234757729 10.90657964 2.562932149
3 - 07 2.066195628 3.382208851 5.648974238 4.281626159 3.215682084 11.288869 2.618612837
3 - 08 2.097007211999999 3.394786347 5.955837369 4.26845752 3.325620187 11.18758306 2.672888563
3 - 09 2.119113885 3.184661935 6.02827852 4.337856855 3.433509426 11.59722985 2.727208709
3 - 10 2.146676079 3.19043647 6.230481448 4.362159458 3.492868264 11.98751269 2.660629317000001
3 - 11 2.213477235 3.27060844 6.278521424 4.396456152 3.496032699 12.12705108 2.623696885999999
3 - 12 2.230953185000001 3.202517912 6.393590754 4.413270061000001 3.557391504 12.2626161 2.615136115
3 - 13 2.279531129 3.177101575 6.49677499 4.419459813999999 3.527810619 12.34938591 2.619545848
4 - 92 2.865957066 4.330514313 5.017279837 4.027419729 3.565646759 9.732693299999998 2.679123430999999
4 - 93 2.917703128 4.368444132999999 5.288267031 4.008836055 3.668266942 9.932599653 2.623757757
4 - 94 2.978029274 4.490329058999999 5.407171771 4.010956794 3.71848877 10.03957318 2.614547784
4 - 95 3.033069934 4.544247449 4.94875989 4.010450866 3.775976419 10.26571804 2.561056773
4 - 96 3.096227528 4.51717088 5.398162701999999 4.035149252 3.725192061 10.49204059 2.457915175
4 - 97 3.129308936 4.535707004 5.187385806 4.044046912 3.760573949 10.65373787 2.407065149
4 - 98 3.046247592 4.751374952 5.262690189 4.204328523 3.283722337 10.7158522 2.588680702
4 - 99 3.085379081 4.798364066000001 5.384495063 4.195682749000001 3.108260412 10.79875603 2.383466597
4 - 00 3.13043067 4.785907529 5.327876168999999 4.192064714 3.29091978 10.87327148 2.151651269
4 - 01 3.264887108 4.7041327 5.602118821000001 4.187621009 3.194510178 10.43328729 2.080811926
4 - 02 3.316347482 4.684954117 5.774551546 4.191143482 3.209929653 10.5332254 2.19556558
4 - 03 3.341720666 4.672300154 5.929589143 4.265025855 3.125153327 10.62589968 2.230668511
4 - 04 3.395256395 4.748171175999999 6.257667588 4.27714531 3.13764477 10.67004415 2.226889365
4 - 05 3.440215253 4.726493407999999 6.257667588 4.229997058000001 3.108909241 10.70233448 2.111701995
4 - 06 3.472905545 4.720156932 6.274762021 4.219010376 3.122516135 10.89134992 2.152925677
4 - 07 3.508038704 4.665029668 6.507277712 4.192184872000001 3.153138499 11.2353607 2.301263088
4 - 08 3.545782451 4.600150797999999 6.706862337 4.190425051 3.066115176 11.20641835 2.299435783
4 - 09 3.508224111 4.51542918 7.118016204 4.182139603999999 2.881193919 11.27713407 2.221277424000001
4 - 10 3.555342632 4.535992034999999 7.115582126 4.202911033000001 3.148296219 11.52899695 2.338060251
4 - 11 3.509382125 4.516506717999999 6.981005741 4.208307868000001 3.145933175 11.65324362 2.466508146
4 - 12 3.529875476 4.445625008000001 7.01571242 4.259807728 3.255217838 11.79551556 2.306748609
4 - 13 3.538300582 4.402807294999999 7.089243155 4.234389291 3.261584574 11.82061875 2.230600839
5 - 92 1.880822837 3.371760604 4.890349128000001 4.289976448 3.060508039 8.296857445 3.082867081
5 - 93 1.880010708 3.445520835 5.181783549999999 4.317032410000001 3.177277311 8.386853044 3.072892012
5 - 94 1.893801639 3.521220695 5.198497030999999 4.332189385000001 3.180667469 8.569952231 3.0912088
5 - 95 1.918607747 3.593394543 5.129898715000001 4.349505767 3.111313986 8.814312889 3.073935201
5 - 96 1.909760055 3.701466337 5.093750201 4.336177311 3.178713014 9.017954782 3.026155821
5 - 97 1.944192215 3.890931781 4.828313737 4.332280827 3.20986572 9.158930334000001 2.937696943
5 - 98 1.925086641 3.801409367 5.093750201 4.313795839 3.152153075 9.328209182 2.691909709
5 - 99 1.929559406 3.81747727 4.9698133 4.309128066000001 2.94223747 9.427784915 2.72202675
5 - 00 2.015989227 3.939040596 5.036952602000001 4.311150708999999 2.910588164 9.529663907 2.636681811
5 - 01 1.964627926 3.829224966 4.905274778 4.313173352 3.097450476 9.248117735999998 2.580388955
5 - 02 1.994935471 3.844750193 5.003946305999999 4.371468973 3.197466902 9.355738575 2.576086137
5 - 03 2.001303914999999 3.853483631 4.94875989 4.398157948 3.13464555 9.342333081 2.541940163
5 - 04 2.060192187000001 3.883060586 5.062595033 4.423746986 3.073230014 9.452266422999997 2.58817274
5 - 05 2.071759345 3.831614901 5.347107531 4.419182116 3.070394658 9.614337737 2.538826711
5 - 06 2.12252613 3.841129548 5.407171771 4.399933471 2.890874537 9.73589696 2.51520422
5 - 07 2.162976212 3.767246873 5.416100401999999 4.400933271999999 2.852862823 9.926373656000001 2.525467458
5 - 08 2.178415909 3.608537799 5.375278408 4.41194593 2.959486165 9.987185112 2.583503863
5 - 09 2.162836383 3.472970715 5.147494477000001 4.437917009000001 2.808894133 10.04024499 2.571136543000001
5 - 10 2.208315997 3.549712007 5.135798437 4.440182345 3.022409465 10.1618438 2.510763449
5 - 11 2.189297296 3.466699355 5.225746674 4.44244768 3.018776971 10.34159553 2.54326682
5 - 12 2.278240305 3.426884138 5.087596335 4.444713015999999 2.895541064 10.50394051 2.471057543
5 - 13 2.339386301 3.328987341 5.393627546 4.446978352000001 2.978676517 10.76376008 2.418559853
6 - 92 2.146341704 3.610173741 4.204692619 3.513347396 3.687977698 9.417488335 2.509382633
6 - 93 2.229365952 3.636521939 4.700480366 3.630580361 3.689123619 9.552582373 2.158549693
6 - 94 2.332346987 3.660266107 5.010635294 3.755271647 3.695197143 9.661497224 2.206983643
6 - 95 2.399355336000001 3.733945847 4.976733742000001 3.871233927 3.739913171 9.780440338 2.251892238
6 - 96 2.445368582 3.6699936 5.313205979 3.995371382 3.733284232 9.888659296 2.251451809999999
6 - 97 2.414119953 3.871404492 5.505331536 4.053844217 3.516415362 9.497983209999999 2.245890372
6 - 98 2.355169994 4.075465168 6.171700597 4.114304202999999 3.017839756 10.14570032 2.377403877
6 - 99 2.379541713 4.06556182 6.603943825 4.12211998 3.02042891 10.34541201 2.239841175
6 - 00 2.405021831 4.201368419999999 6.329720906 4.129935756 3.128346376 10.3399344 2.199792592
6 - 01 2.402220073 4.187479687 6.280395839 4.137751533000001 3.182142339 10.43699175 2.212027019
6 - 02 2.424668348 4.161914959 6.421622268 4.164929299 3.169747992 10.56560835 2.244404464
6 - 03 2.470310977 4.184804253 6.687108608 4.182632113999999 3.217612811 10.81380045 2.342532905
6 - 04 2.505924461 4.258403826000001 6.708084084 4.200334929 3.288062838 10.89416193 2.332730143
6 - 05 2.535843439 4.298206215999999 6.792344427 4.267862621 3.44815336 11.02538204 2.328750397
6 - 06 2.573442433 4.299281985000001 6.946975992 4.270809838 3.342766428 11.26640678 2.376426014999999
6 - 07 2.606974873 4.296257305 6.851184927 4.347196549000001 3.274646368 11.45825244 2.367970728
6 - 08 2.610571854 4.336566308 6.80461452 4.357234073 3.371566011 11.45666503 2.447590045
6 - 09 2.568536679 4.224666141 6.932447892 4.390355601999999 3.055928148 11.57996967 2.439272076
6 - 10 2.635000975 4.266696665000001 7.101675972 4.424592588000001 3.255596336 11.84428486 2.516745657
6 - 11 2.624599798 4.343071632 6.831953566 4.470192262 3.281907039 11.95141233 2.59092785
6 - 12 2.675175973 4.317218518 6.927557906 4.465712006999999 3.392523448 12.05798448 2.50714843
6 - 13 2.704552764 4.298195186 7.360103973 4.453270437999999 3.375547464 12.09099101 2.483461332
7 - 92 1.902884364 2.854128491 2.833213344 3.302693307 3.007499764 6.179172198 3.271343317
7 - 93 1.892256361 2.79156228 2.944438979 3.28253377 3.035830698 6.207869847 3.21863661
7 - 94 1.904450318 2.79009186 3.433987204 3.262374232 2.972792331 6.230067976 3.240645677
7 - 95 1.949167991 2.816005527 3.044522438 3.242214695 2.920228431 6.397079644 3.263419144
7 - 96 1.97267076 2.827497142 2.772588722 3.222055158 2.944263035 6.723459775 3.238005212
7 - 97 1.923293759 2.777697726 3.295836866 3.201895621 2.885872571 7.249215057 3.284759676
7 - 98 1.899090441 2.802438209 3.80666249 3.181736083 2.874197188 7.513709248 3.307255267
7 - 99 1.912552961 2.731343417000001 3.784189634 3.161576546 2.745021106 7.544644229 3.297006325
7 - 00 1.966521884 2.598333889 3.828641396 3.141417009 2.846456465 7.678677984 3.255375929
7 - 01 1.964418547 2.685091292 4.060443011 3.121257472 2.832998965 7.69721214 3.18199944
7 - 02 1.957578802 2.722847985 4.007333185 3.101097934 2.808363701 7.804124897 3.150763276
7 - 03 1.984182034 2.816543848 4.043051268 3.080938397 2.818876868 7.964145288 3.151140971
7 - 04 2.012472675 2.751550175 4.290459441000001 3.182134659 2.808077207999999 8.036146410000002 3.099349064
7 - 05 2.063412734 2.752991408 4.96284463 3.230046797 2.948706457 8.171076597000001 3.066442556
7 - 06 2.0316694 2.648580612 4.510859507 3.426002183 2.961761722 8.339794696 3.135898326
7 - 07 2.031252758 2.581323294000001 4.691347881999999 3.49832258 2.933168831 8.389082509 3.138024733
7 - 08 2.032743386 2.516268912 5.135798437 3.51048398 2.955214659 8.47970314 3.140444076
7 - 09 2.014002305 2.517353867 4.955827057999999 3.512257722 2.865024346 8.57147681 3.174218561
7 - 10 2.013058731 2.603893986 4.736198447999999 3.528440125 2.760298377 8.711454847 3.19015169
7 - 11 2.028337394 2.636673752 4.521788577 3.55353957 2.647688976 8.726774794000002 3.259045494
7 - 12 2.037397161 2.517427304 4.564348190999999 3.600072284 2.713101316 8.955479090000001 3.200674837
7 - 13 2.066658401999999 2.581730169 5.017279837 3.645904362 2.678911358 9.058760219 3.223385108
8 - 92 1.159090588 2.026393671 4.276666119 3.198358307 2.850997169 6.767207788 3.380476584
8 - 93 1.183126349 2.199141521 3.583518938 3.293670973 2.88741361 6.969196885 3.267724989
8 - 94 1.204508398 2.197396079 3.663561646 3.388983638 2.912489578 7.114592522 3.24370422
8 - 95 1.230183629 2.385512947 4.248495242 3.484296304 2.950724229 7.166097929 3.272770138
8 - 96 1.253623888 2.272796552 3.091042453 3.579608969 3.031579544 7.26387775 3.19751323
8 - 97 1.267376656 2.353313431 3.828641396 3.674921635 3.082653478 7.528752323 3.196980132
8 - 98 1.289712469 2.46447698 3.465735903 3.770234301 3.096546045 7.606279179 3.167825695
8 - 99 1.311160631 2.464588492 3.891820298 3.837748712 3.123305905 7.701553985 3.16935359
8 - 00 1.328901213 2.513186417 4.248495242 3.866944046 3.170045288 7.375387183 3.168539602
8 - 01 1.332788031 2.594251159 4.077537444 3.89209169 3.185290364 7.324265304 3.135289107
8 - 02 1.332985377 2.518500096 3.761200116 3.914305199 3.192179267 7.446094434 3.075858045
8 - 03 1.333890672 2.436341971 4.060443011 3.908926817 3.205960332 7.571411446 3.040913744
8 - 04 1.361524863 2.41112635 3.871201011 3.836918137 3.218549129 7.684728405 3.011238385
8 - 05 1.405822348 2.666730992999999 3.912023005 3.807187019 3.251553468 7.802568698 2.975522823
8 - 06 1.446625566 2.794439714 3.091042453 3.819650447 3.263625293 7.981733287 2.944880965
8 - 07 1.477604161 2.832938886 3.36729583 3.83668289 3.264938351 8.169166458 2.928849365
8 - 08 1.501653643 2.871237554 4.094344562 3.794590358 3.265846102 8.362315581000001 2.916268886
8 - 09 1.521442108 2.829685535 4.007333185 3.874814059 3.265990568 8.452462652 2.883055232
8 - 10 1.543848266 2.774094631 4.189654742 3.909789914 3.267538597 8.51887215 2.879788084
8 - 11 1.573282648 2.991828411 3.610917913 3.927776269 3.311308172 8.697763523000001 2.874308276
8 - 12 1.603252759 3.003779253 4.204692619 3.982460779 3.34153 8.916469963999999 2.838724771
8 - 13 1.628443986 2.972354844 4.094344562 3.955492277 3.346023612 9.100466607 2.789695981
Thanks,
Regards.
Re: panel group FMOLS
How about the EViews output? Please indicate what you think is wrong.
I hope you misunderstood what they said. The calculation of the LR variance isn't "tedious". It does, however, depend upon the number of lags and that choice does affect the estimates. However, from what I can see, the individual results aren't that sensitive to the difference between 2, 3 and 4 lags and the grouped results are quite similar.
If you're comparing the individual estimates produced by RATS with grouped estimates produced by EViews, then you would not get the same (or even necessarily similar) results. You don't have much data per individual for a six variable model, so the individual results would be expected to vary quite widely. The equivalent RATS grouped estimates are the final ones displayed.
I hope you misunderstood what they said. The calculation of the LR variance isn't "tedious". It does, however, depend upon the number of lags and that choice does affect the estimates. However, from what I can see, the individual results aren't that sensitive to the difference between 2, 3 and 4 lags and the grouped results are quite similar.
If you're comparing the individual estimates produced by RATS with grouped estimates produced by EViews, then you would not get the same (or even necessarily similar) results. You don't have much data per individual for a six variable model, so the individual results would be expected to vary quite widely. The equivalent RATS grouped estimates are the final ones displayed.
Re: panel group FMOLS
Thank you Tom for your reply and suggestions! They will be really helpful in resolving my problems.
Yes you are right that the grouped results from the two softwares are quite similar but the same thing isn't true for individual cross-sections.In particular,for cross-section 8,the coefficient of X4 is 0.19(approx.) by Eviews while it is -.08(approx.) by RATS.Also for the same cross-section,coefficient of X3 is -.08(approx.) by Eviews while it is .03(approx.) by RATS.Although there are differences in the results for other cross-sections as well,but they don't affect my major findings per se. But the difference for this cross-section does make a qualitative difference in results as well.Please see to it and suggest.
Following is the Eviews output for the group as a whole(I am not reporting individual cross-sections' results):
Dependent Variable: Y
Method: Panel Fully Modified Least Squares (FMOLS)
Date: 03/16/16 Time: 12:41
Sample: 1992 2013
Periods included: 22
Cross-sections included: 8
Total panel (balanced) observations: 176
Panel method: Grouped estimation
Cointegrating equation deterministics: C
Long-run covariance estimates (Bartlett kernel, Newey-West fixed
bandwidth)
Variable Coefficient Std. Error t-Statistic Prob.
X1 0.064494 0.023733 2.717518 0.0073
X2 0.073621 0.011613 6.339538 0.0000
X3 0.433788 0.040694 10.65971 0.0000
X4 0.065894 0.022666 2.907209 0.0042
X5 0.067229 0.005972 11.25763 0.0000
X6 -0.106335 0.033559 -3.168569 0.0018
R-squared -14.432935 Mean dependent var 2.066967
Adjusted R-squared -15.671381 S.D. dependent var 0.608939
S.E. of regression 2.486336 Sum squared resid 1001.462
Durbin-Watson stat 0.000338 Long-run variance 0.000313
Yes you are right that the grouped results from the two softwares are quite similar but the same thing isn't true for individual cross-sections.In particular,for cross-section 8,the coefficient of X4 is 0.19(approx.) by Eviews while it is -.08(approx.) by RATS.Also for the same cross-section,coefficient of X3 is -.08(approx.) by Eviews while it is .03(approx.) by RATS.Although there are differences in the results for other cross-sections as well,but they don't affect my major findings per se. But the difference for this cross-section does make a qualitative difference in results as well.Please see to it and suggest.
Following is the Eviews output for the group as a whole(I am not reporting individual cross-sections' results):
Dependent Variable: Y
Method: Panel Fully Modified Least Squares (FMOLS)
Date: 03/16/16 Time: 12:41
Sample: 1992 2013
Periods included: 22
Cross-sections included: 8
Total panel (balanced) observations: 176
Panel method: Grouped estimation
Cointegrating equation deterministics: C
Long-run covariance estimates (Bartlett kernel, Newey-West fixed
bandwidth)
Variable Coefficient Std. Error t-Statistic Prob.
X1 0.064494 0.023733 2.717518 0.0073
X2 0.073621 0.011613 6.339538 0.0000
X3 0.433788 0.040694 10.65971 0.0000
X4 0.065894 0.022666 2.907209 0.0042
X5 0.067229 0.005972 11.25763 0.0000
X6 -0.106335 0.033559 -3.168569 0.0018
R-squared -14.432935 Mean dependent var 2.066967
Adjusted R-squared -15.671381 S.D. dependent var 0.608939
S.E. of regression 2.486336 Sum squared resid 1001.462
Durbin-Watson stat 0.000338 Long-run variance 0.000313
Re: panel group FMOLS
Dear Tom,
Can the assumption about covariances of coefficients(that is whether they are homogeneous or heterogeneous) be a reason for the differences in results?
Regards.
Can the assumption about covariances of coefficients(that is whether they are homogeneous or heterogeneous) be a reason for the differences in results?
Regards.
Re: panel group FMOLS
The first line in the description of @PANELFM (with emphasis added):
AVERAGE=[SIMPLE]/SQRT/PRECISION
option. It sounds like EViews is using some other method of estimation. You need to read their documentation more carefully to see what they're doing.
The Pedroni procedure is to estimate the cointegrating vectors separately, then aggregate them using one of three methods, controlled by the@PANELFM is a procedure for estimating the cointegrating vectors using the multivariate group mean panel FMOLS from Pedroni(2000) "Fully Modified OLS for Heterogeneous Cointegrated Panels," Advances in Econometrics, Vol. 15, 93-130, NONSTATIONARY PANELS, PANEL COINTEGRATION AND DYNAMIC PANELS, JAI Press.
AVERAGE=[SIMPLE]/SQRT/PRECISION
option. It sounds like EViews is using some other method of estimation. You need to read their documentation more carefully to see what they're doing.
Re: panel group FMOLS
Dear Tom,
What is the program for performing pooled panel DOLS?
Thanks.
Regards.
What is the program for performing pooled panel DOLS?
Thanks.
Regards.
Re: panel group FMOLS
That isn't a well-defined request. @PANELDOLS does group mean panel DOLS which is similar to what I described above for @PANELFM.
Re: panel group FMOLS
Actually, I wanted the program for pooled version of panel DOLS that assumes homogeneity of cointegrating vectors across cross-sections.
Regards.
Regards.
Re: panel group FMOLS
The grouped estimator in @PANELDOLS (and @PANELFM for that matter) assumes homogeneity. They just don't assume homogeneity in the other aspects of the model (short-run dynamics and variances). If you impose homogeneity too quickly, the results may be dominated by higher variance cross sectional units. Have you read Pedroni's papers?