The RATS Software Forum

Posted: **Mon Mar 14, 2016 9:11 am**

What's special about the cross section that doesn't seem to work? Missing data?

Posted: **Tue Mar 15, 2016 3:11 am**

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.

Posted: **Tue Mar 15, 2016 3:39 am**

Dear Tom,
Can we retrieve p-values for individual coefficients?

Thanks.
Regards.

Posted: **Tue Mar 15, 2016 7:48 am**

sanjeev wrote:Dear Tom,
Can we retrieve p-values for individual coefficients?

Thanks.
Regards.

You can retrieve the individual t-statistics (%%ITSTATS) , so you have the information needed.

Posted: **Tue Mar 15, 2016 8:11 am**

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.

You would have to post the program and data and what you did with EViews. Did you ask the people at EViews about this?

Posted: **Tue Mar 15, 2016 11:27 am**

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.

Posted: **Tue Mar 15, 2016 11:47 am**

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:

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

Please help me out!

Thanks,
Regards.

Posted: **Tue Mar 15, 2016 12:26 pm**

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.

Posted: **Wed Mar 16, 2016 2:20 am**

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

Posted: **Wed Mar 16, 2016 2:54 am**

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.

Posted: **Wed Mar 16, 2016 8:20 am**

The first line in the description of @PANELFM (with emphasis added):

@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.

The Pedroni procedure is to estimate the cointegrating vectors separately, then aggregate them using one of three methods, controlled by the

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.

Posted: **Thu Mar 17, 2016 1:32 am**

Dear Tom,
What is the program for performing pooled panel DOLS?

Thanks.
Regards.

Posted: **Thu Mar 17, 2016 7:24 am**

That isn't a well-defined request. @PANELDOLS does group mean panel DOLS which is similar to what I described above for @PANELFM.

Posted: **Thu Mar 17, 2016 11:17 am**

Actually, I wanted the program for pooled version of panel DOLS that assumes homogeneity of cointegrating vectors across cross-sections.

Regards.

Posted: **Thu Mar 17, 2016 12:57 pm**

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?

The RATS Software Forum

panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS

Re: panel group FMOLS