How to get residuals series of many regressions?

[priya sawaliya]

Hello,
I want to run 500 regression in one command. dependent series are Y1 to Y500 and three independent variables x1, x2 and x3. following is the command (please make me correct if i am wrong):
foreach y of varlist y1-y500 { regress `y' x1 x2 x3 } My query is how I get residuals series of each regression. Please help me..

Thank you
Priya

[Wouter Wakker]

forval i = 1/500{
    reg y`i' x1 x2 x3
    predict resid`i', resid
    }

[Attaullah Shah]

Your data seems to be in wide format. You can do much more and quite easily with data in a long format. In the following lines of code, I first generate a dummy data in wide format, then reshape it to a long format and then use asreg to estimate regressions for each y group with residuals. asreg can be installed from ssc and is superfast compared to estimating regressions in a loop.

* create a dummy data set in wide format.
set obs 30
gen x1 = uniform()
gen x2 = uniform()
gen x3 = uniform()
forv i = 1 / 500 {
gen y`i' = uniform()
}
gen i = _n

* reshape the data to a long format
reshape long y, j(d) i(i)

* install asreg
ssc install asreg

* estimate regressions with residuals
bys d: asreg y x1 x2 x3, fit

list d x1 x2 x3 y _residuals in 1/10

     +------------------------------------------------------------+
     | d         x1         x2         x3          y   _residuals |
     |------------------------------------------------------------|
  1. | 1   .6866152   .0442154   .2297006    .872496    .31195766 |
  2. | 1   .1366463   .4884419   .6795634    .894106     .2061596 |
  3. | 1   .5551032   .9805113   .6537739   .5858005    .03577482 |
  4. | 1   .7264384   .1729307   .3200437   .5346152    -.0070024 |
  5. | 1   .4961259   .2190997    .851468   .2775184   -.31307716 |
     |------------------------------------------------------------|
  6. | 1   .3488717    .859742   .6964867   .0324792    -.5799762 |
  7. | 1   .0674011   .7720399   .0360847   .9339853     .2188379 |
  8. | 1   .1196613   .6934533   .3462876   .6546342   -.03976109 |
  9. | 1   .8506309    .748933   .0030522   .7970893    .30416002 |
 10. | 1   .0285569   .8712187   .3549416   .9684734    .25511015 |
     +------------------------------------------------------------+

Want to read more on asreg, read this one page introduction https://fintechprofessor.com/stata-p...ions-in-stata/

[priya sawaliya]

Thank you very much Wakker and Shah sir for your reply.
@Shah sir,
what does this command mean: set obs 30 and why we are writing this?

[Carlo Lazzaro]

Priya:
if you do not -set- the number of observations before -generate- a given variable, nothing will be reported in Stata, as you can see from the following toy-example:

. gen A=10

. list A

. set obs 3
number of observations (_N) was 0, now 3

. gen B=10

. list

     +--------+
     | A    B |
     |--------|
  1. | .   10 |
  2. | .   10 |
  3. | .   10 |
     +--------+

.

[priya sawaliya]

Thanks Carlo sir.

My data is monthly time series from July 2005 to September 2017 which means 147 observations in each series. My dependent variable is Y1 to Y500.

1. My first query is when I write the command "set obs", what should I write? Should I write set obs 147 or set obs 73,500 (147*500) as in long format it has 73,500 observation.
2. My second query is should this command "asreg y x1 x2 x3" will run seperate regression for each Y on x1, x2 and x3? Thank you

[Attaullah Shah]

Post your real dataset or a sample out of it using the dataex (can be downloaded from ssc). In my post, I generated a fake dataset to show the application of asreg to it. Since you have your data, you do not need to generate fake data.

ssc install dataex
dataex

Concerning asreg, the code was

 
 bys d: asreg y x1 x2 x3, fit

the prefix bys d: is used here for estimating the regression separately for each subset of data. The variable d tracks that subset. In short, yes to your question.

[priya sawaliya]

Thank you Shah sir for your reply.
with the help of command " bys d: asreg y x1 x2, fit" I am able to get residuals for my data. Now it is clear to me that there is no need to set obs command.
But when I separately run regression for each Y in MS excel or in Eviews. then the residuals are different which i got from asreg command. The residuals values should be same. So why this happening sir ?
My dataset (where there are Y1 to Y3 are dependent variables and X1, X2 are independent variables, from July 2005 to September 2019) is look like:

* Example generated by -dataex-. To install: ssc install dataex
clear
input float monthlyDate double(X1 X2 Y1 Y2 Y3)
690     .02638653981399447    .04302490000452557    .01199451449561055   -.007354913030727411    -.02418466374439846
602    -.02178811221601447    .05703899770053844    .10113564274026064  -.0012868477955049057    .005632532115114254
591   -.015240431107463814    .17603997237102656   .029633554946398324    -.06921071593063938     .00359271903041411
651   -.011748756048242937  .0030561088061163517    .08712111546927844    .005864275287080105    -.02130456702239847
554     .05519409044366356    .07938988738776595   .048599785130715355     -.0215485357106322    .058114020511252806
671    .016125836176913457  -.006492658868437734   .039548706776615136  -.0005165870150741596   -.006439038578125103
587   -.026089120277655767     .0868088066994298    .28341777448439803     -.0629306946738789    -.10237242803888211
654   -.026204988272239313   .002298270496052644   .004556018344032083    -.05093840860490639     .08260051893049211
619     .07528509252174581   -.09884788674467222   -.08791482856471086    -.02372926597412328     .06463909979052998
607     .03233566489935693   .010829973861835443  -.028356607396660984   -.005122831126895943    .014175146734359035
590   -.005353366267338983    .06473041819611364    -.1380127046527298   -.014777751389460816   .0005976215527972728
588    .006836976103741996   -.05569592714999593   -.13292565618302737     .06720384065337162     .07096871683974276
559   -.010663197611191375    .10012475363679617     .6138383504301341  -.0030819755230080542   -.047539362335306395
571  .00014665046483598038  -.021215659763704795  -.010029176476919536     .02559821914147855   -.006869664423925871
568    .005371307522736346    .04630626969046031     .2978263998858029    .010815593131422595   -.011974184382104121
639   -.028360787912422445   .035210373750927254     .0924971944406468    .004835456878866509    -.05529234292741607
664   -.032717128743427124   .023562257460714275    .00952692476800096    .003409199140017881    .025757292110209894
600     .01081035470306235   -.04797397323122907   .041397489961143445    .020297511853228926    .023064808226914157
662    -.02621815330753055   -.04376360977089904   .012116173498547121   -.012852023333211202     .04561433171524626
551     .03743972708419209    .05673835105812602    .02444288926497208   -.028267714534130836     .03126106231484681
648     .05857214065069627  -.054650459375545905    -.0714837064745814   -.033336907816556516     .06913038227486797
613    .022041879279909124   -.04701471777306566   -.09050393565310812   -.017593657972898025     .03483216015125226
687   .0009341924426899684   .024074797507858174   .049812602781141316   .0048682141058381395    -.03338163770846861
603  -.0026749459129589426    .01319598637195019    .05879052525623885    .004819294166790847    -.01620245139239078
617    .002292519646038116   .013335449092528293    .03905907762502119    .016199090424298197     .03656149866142937
567   -.006542359573396203    .06318491701805779    .39937681769539446    .006836903850457876   .0005197651735329087
681    .011045282698392327   .009821562743024426    .07665117658736546    .019383461329673807    -.01417848863896825
630     .04256730726696051  -.023460627672218178    .12102905667043336    .004873106591381397    .024643735331878892
549   -.022863161517496483   -.12522983219976278   -.11485709121494228   -.014680835045026375     .03687451392448621
599   -.025368515827457194    .03009502036576156    .12865729752333022     .00597939834411447 -.00033689369900467836
656   -.004030720284598794  .0005153963031577242   .037877046487306026     -.0324277613270843      .0676025133259136
578    -.07021234613949395   -.11167069862118478   -.30975155067820626   .0011562559886275914     .05628870558312554
629    -.03342276756980009    .05359918581349932    .04523122943114229    .008740337592505386   -.013737544572640488
546     .08552669104735565    .06036433256512017    .10176730958223186    .005530315243593917   -.028201042575244292
668    .016585048315293116  -.012657700081089145  -.046716976483478515    -.02197503350019346   -.012461023210168749
672   -.005000929676929167   -.06068374686373203    -.1303547142381174   -.013309229706812142    .031039289300905548
641     .07531176730965757   -.06324212280779057   -.08631595469304748   -.013346064604485577     .04855476780192816
622     .04450613520536156   -.11057252838530636    -.1897650862340316   .0069532070773171695     .03046417906533587
608   -.007586483195828511    .08142832961465962   .026493448956905082    .010438619405220145    .017403401061928586
618     .04901408260872691  -.028421368469604805   .000991883229465927   -.009210971326104208    .009350164403755716
582     .03288574246507414    .04793964322505168    .10249480738672481     .01699231314883489   -.049108782893319644
593 -.00021012019784896058  -.007888698736820772  -.046527242171121724   -.010133922017366449 -.00014683299534811595
675    .004993843661136574   .018452818898229754  -.012876241190474348    .006129502304328557   -.011169632690305055
645     .02980941962626038    .07155093272715338    .25640017443298313   .0046376743111619795   -.031874893883960795
644  -.0039003604138840298    .04246521644515159     .0625576463495023    .018534767940747973    .008309102211406669
550    .051479441833352874    .12893483354645477    .05557549993576867    .014795881079068263    -.01860680865488873
653   -.017988713611505558   .054506873228346325    .13760913835343574  -.0030588841295529495    .032995880757778234
574    -.00567596034408668   .010699400926707365    .23340552409156107     .07282888361788797    -.10083785983002627
566   -.010964581745386299  -.003148218622815594    -.4574809806102459     .02175667458336428   -.011295527828339449
614    .007716576704352149    .07077136479502687    .07524872131440774    .025152189473045837    -.00465371915554242
643   -.041569636128964596   -.05763921166582483   -.07681599214716367    -.02701807956516991   -.008858732746112631
673  -.0021483513888521932   -.09025624182024944   -.12921635971637105   -.020331702814306962     .03442311310514466
583    -.02872260182126808   .013475433883809333    .08632104823815763    .006919576585663136     .03706562533308071
598    .022551417168261964    .06684136864799514   -.01840273395416842   -.021793479718617975   -.004849499678043573
686    .015634584741165948   .029723456687892644    .06775465753284392   -.006930056713837292   .0057763376210025474
561     .01901656009143597    .04372707149261758    -.0861453142692353    -.05571256004788088   .0017360698010594408
692    .013341542277384828  -.012819470909322433 -.0032750748757669357   .0005385497551796308    .023442463212276374
564    .006115906854628414    .02888980577738069    .34737739820492763   .0019081101899439817  -.0019201917044298147
667     .02680678547946118   -.05498795950590314    .11016704828435023    -.04783757809015148     .06101256245974376
586   -.027273638483687093  -.022510741213680175  -.029886702096483107     .09647735774974356   -.002455061896756046
605    .010370519092942482   .048199239240456465    .13720399387160262    .015963940726483765     -.0130644660396627
609      .0557251559220764  .0008337146951032286    .04948762600382413     .02104929782605215    .009795087844231075
565    .012990668883768625   -.10661869544060737    -.5277030336324345     -.0240622501875922    .027748191709931314
552    .011924221572189309   .042316470227539316   -.02927035799875277  -.0024345100034403114    -.01082960416715676
604     .03902535249228272   -.04979168018370136    .03822085498507351    .010549351499692272    .025550168542276097
657    .019503528989967243   .016646044334754688    .02273580696138851    .028579370849135817    -.02716287602925641
572     .03717401895627939    .13897481627087502    .14337909634829482    .021679814337936795    -.05508672450569002
642     .06409478774769009   -.03262351080297927   -.06165424065336029   -.028278786409560563     .09175053604930858
674   -.004138671883346934    .09234949115640868    .08058951195802207     .02930336394977255    -.03383644374298925
659   -.021502449570329644  -.034094775679757584    .03613435135019154    .024070232848613947    .030005058736263808
601    .019549330341170033  -.002142716311355584 -.0028689604157677824     .02441442443602735    .010398688994791877
589    -.03602204128768939    -.0624370336089164     .1416401220692592    .037813037422039036     .05217611959254319
631     .06263318146909266   .006989993709272336    .07857907960728933   -.014531398291101021     .04987303506518417
649    .020290049437099077    .02718906519444749    .06128602112441317   -.013991290184660526    .035133428814462964
637      .0912300551041374   -.07465854799733722   -.06333466635090926    -.03690983310362672     .06262495718247044
634     .00407863447274448    .04724201290481994   .018902453366939666   -.014773973738321365   -.003449050645425434
633    .030238466137313593  -.024611873128540324  -.027944810897520315   -.001108629027275692    .008966890081646894
555    .051390158506607525    .06716598877791746  .0023238717033960887   -.014396530991192757   -.020890765549194903
652    -.11563789048102016    .09133088806335173    .19700965949096663     .02597812526408866     -.1446294893511154
615    .004377900687003302  -.002295134207331475    .05396326708824468     .01263858498210987    .018705215912480165
557   -.034471840857198566    -.0695661931793848    -.2386851701754755   -.014215971105794867     .01864040164908777
606     .04600416096976601   .011029841399184625   .030069513806459596    .010671871567129822     .02216896538987303
625    -.04301509906045406   .058772026205247026     .3070455329549663    .006528308729473964   -.018348375763223873
575     -.0574322832815171    .07110871246441276    .21336627922594442     .02543708959167501   -.009482323596968961
560    -.02452440092652386    .05808028761923638      .521230046145797   -.018011496503149267   -.030213544570864774
547    .009021602492996023    .03289669921581907    .13207283896475544 -.00035280272487685227   -.026888626812923436
646   -.018091393555606707  -.007014592840457459    .03776713462501391     .01956281800088349   -.023867855824530965
627    .056934072813931466   -.01607854122378937    .10256275067921926     .02639640924035069     .03161198554183506
665   -.014587079517742095  -.017370603335194645   -.03067210625106378   -.002706394585948787    .009282654341920958
579    .014977526289855742    .06763281222150865     .1162708738109869   -.005368599423631132     .01713802993413449
628     .03741159308567446   -.06991795306912295   .026027079838056875    .006689160666173352    .011738521018555909
581     -.0216037883787642   -.22360835591589712   -.13362782971439707    .026899279963932765      .0918494294176769
592    .028915357318765045     .2848624171515663     .5188514197289154    -.09855817702784686    -.09740697367425599
558   -.030505334747280267  -.012163209987963452    -.2803713222762772    .036356102868748984    -.02073580332225087
689    .013502165696492645 -.0036984951337338096  -.015745095109296607    -.01842252498803304    -.01856562785297696
650    -.03981734945977418    .06102077314509861    .14547461408996673     .07028878707210094    -.06877305377624843
621   .0038781484704318303    .05302488060379295    .04154195377201581     .00945019362030132    .002287531574314279
594    .015852454556496004    .06197687293170169    .07741921459723611   .0013670470454574535    .031071570536560983
684     .00225570894793993    .05947560272925179    .05505877320324332    .019552526512571708     -.0359727216239006
632    -.04108747339008603    .07153832178946122    .09946779639858551   .0040499912603234045    -.03955561877371175
end
format %tm monthlyDate

Thank you
Priya

[Attaullah Shah]

The data you have posted is still in wide format. In order for asreg to work without a loop, you need to change it to a long format. Here is an example of using wide-format regression and comparing it with the official regress command of Stata.

asreg Y1 X1 X2, fit

* And confirm using reg command
reg Y1 X1 X2
predict residual_ols, res

list  _residuals residual_ols in 1/10

     +------------------------+
     | _residuals   residua~s |
     |------------------------|
  1. | -.06921398    -.069214 |
  2. | -.01162675   -.0116267 |
  3. |  -.2570014   -.2570014 |
  4. |   .0561777    .0561777 |
  5. | -.07967452   -.0796745 |
     |------------------------|
  6. |  .02898477    .0289848 |
  7. |  .12581448    .1258145 |
  8. | -.02854256   -.0285426 |
  9. |  .05100126    .0510013 |
 10. | -.06077763   -.0607776 |
     +------------------------+

* And after reshaping the data

reshape long Y, j(var) i(monthlyDate )
bys var :  asreg Y X1 X2, fit

sort var monthlyDate

list  _residuals residual_ols in 1/10

     +------------------------+
     | _residuals   residua~s |
     |------------------------|
  1. |  .00839338    .0083934 |
  2. |  .06185861    .0618586 |
  3. |  .04072039    .0407204 |
  4. | -.14654675   -.1465468 |
  5. | -.07447146   -.0744715 |
     |------------------------|
  6. | -.11270819   -.1127082 |
  7. | -.07967452   -.0796745 |
  8. | -.10879878   -.1087988 |
  9. | -.16775852   -.1677585 |
 10. |  -.2931334   -.2931334 |
     +------------------------+

[priya sawaliya]

Dear sir, the dataset which I have share in my above post, has been changed completely may be because of using "monthlyDate". Therefore I am posting the dataset in correct form below:

* Example generated by -dataex-. To install: ssc install dataex
clear
input int Date double(X1 X2 Y1 Y2 Y3)
16647     .08552669104735565   .06036433256512017    .10176730958223186    .005530315243593917   -.028201042575244292
16678    .009021602492996023   .03289669921581907    .13207283896475544 -.00035280272487685227   -.026888626812923436
16709     .03220915637771924   .07781224428689261   -.09360033485197984   .0022163791399358312    .017852734033312084
16739   -.022863161517496483  -.12522983219976278   -.11485709121494228   -.014680835045026375     .03687451392448621
16770    .051479441833352874   .12893483354645477    .05557549993576867    .014795881079068263    -.01860680865488873
16800     .03743972708419209   .05673835105812602    .02444288926497208   -.028267714534130836     .03126106231484681
16831    .011924221572189309  .042316470227539316   -.02927035799875277  -.0024345100034403114    -.01082960416715676
16860    .051501013954253924   .03089437580327755  -.018813101296595414     .03295111322418927    -.04982170243066069
16890     .05519409044366356   .07938988738776595   .048599785130715355     -.0215485357106322    .058114020511252806
16921    .051390158506607525   .06716598877791746  .0023238717033960887   -.014396530991192757   -.020890765549194903
16951    .010352655446916659  -.12153744053933128   -.13452422524406066    -.07100056943094779     .03806916904848953
16982   -.034471840857198566   -.0695661931793848    -.2386851701754755   -.014215971105794867     .01864040164908777
17012   -.030505334747280267 -.012163209987963452    -.2803713222762772    .036356102868748984    -.02073580332225087
17043   -.010663197611191375   .10012475363679617     .6138383504301341  -.0030819755230080542   -.047539362335306395
17074    -.02452440092652386   .05808028761923638      .521230046145797   -.018011496503149267   -.030213544570864774
17104     .01901656009143597   .04372707149261758    -.0861453142692353    -.05571256004788088   .0017360698010594408
17135    .020437057433581138  .043170981583634756    .18454479985195846    -.04951115257453025    -.01407808752377145
17165     .01399500769638593 .0022092485728934215   -.18264655405171284    -.00827920887080089   -.009910676634185683
17196    .006115906854628414   .02888980577738069    .34737739820492763   .0019081101899439817  -.0019201917044298147
17225    .012990668883768625  -.10661869544060737    -.5277030336324345     -.0240622501875922    .027748191709931314
17255   -.010964581745386299 -.003148218622815594    -.4574809806102459     .02175667458336428   -.011295527828339449
17286   -.006542359573396203   .06318491701805779    .39937681769539446    .006836903850457876   .0005197651735329087
17316    .005371307522736346   .04630626969046031     .2978263998858029    .010815593131422595   -.011974184382104121
17347   -.021981543253431463   .02622886905143026    .45015590785903453     .00964872398393627  -.0004636804084227203
17377  -.0009468708583040397  .024999260060612486   -.10556040220179633   -.005216345125710514    -.02191967541179973
17408  .00014665046483598038 -.021215659763704795  -.010029176476919536     .02559821914147855   -.006869664423925871
17439     .03717401895627939   .13897481627087502    .14337909634829482    .021679814337936795    -.05508672450569002
17469     .06620914107063097   .12646630177027035     .2241770999604365   -.018313132186904314    -.04554665277154857
17500    -.00567596034408668  .010699400926707365    .23340552409156107     .07282888361788797    -.10083785983002627
17530     -.0574322832815171   .07110871246441276    .21336627922594442     .02543708959167501   -.009482323596968961
17561    .049277636708733835   -.1703807680372463   -.08654996872590295   -.010445952214747645     .03630035336038728
17591    -.04397105202612482 -.020417533309253992  -.058682047571174435     .03852672202686533      .0312987302531418
17621    -.07021234613949395  -.11167069862118478   -.30975155067820626   .0011562559886275914     .05628870558312554
17652    .014977526289855742   .06763281222150865     .1162708738109869   -.005368599423631132     .01713802993413449
17682   -.023850682900881798  -.06763626509936518  -.036573779073710594    -.01790523705949943     .03448793243961419
17713     -.0216037883787642  -.22360835591589712   -.13362782971439707    .026899279963932765      .0918494294176769
17743     .03288574246507414   .04793964322505168    .10249480738672481     .01699231314883489   -.049108782893319644
17774    -.02872260182126808  .013475433883809333    .08632104823815763    .006919576585663136     .03706562533308071
17805   -.005365540788792644   -.1467692815060055   -.24279875607273377     .06541346370665901    .016483441972782842
17835    .024715663852189096   -.3857939273842153    -.4828378218592362     .06355714967860682     .03715344982230484
17866   -.027273638483687093 -.022510741213680175  -.029886702096483107     .09647735774974356   -.002455061896756046
17896   -.026089120277655767    .0868088066994298    .28341777448439803     -.0629306946738789    -.10237242803888211
17927    .006836976103741996  -.05569592714999593   -.13292565618302737     .06720384065337162     .07096871683974276
17956    -.03602204128768939   -.0624370336089164     .1416401220692592    .037813037422039036     .05217611959254319
17986   -.005353366267338983   .06473041819611364    -.1380127046527298   -.014777751389460816   .0005976215527972728
18017   -.015240431107463814   .17603997237102656   .029633554946398324    -.06921071593063938     .00359271903041411
18047    .028915357318765045    .2848624171515663     .5188514197289154    -.09855817702784686    -.09740697367425599
18078 -.00021012019784896058 -.007888698736820772  -.046527242171121724   -.010133922017366449 -.00014683299534811595
18108    .015852454556496004   .06197687293170169    .07741921459723611   .0013670470454574535    .031071570536560983
18139    -.04280537100673845   .03661271208077691  -.017646585785140587   -.013639861834037703    .027221384363668957
18170    .007132570856523815   .06984252346999685    .15390322098648554    .027653866926790865   -.041680707880628734
18200     .06600407151950623  -.06735996286064377     .1019890282771319     .01655381326703087    .053215462527011384
18231    .022551417168261964   .06684136864799514   -.01840273395416842   -.021793479718617975   -.004849499678043573
18261   -.025368515827457194   .03009502036576156    .12865729752333022     .00597939834411447 -.00033689369900467836
18292     .01081035470306235  -.04797397323122907   .041397489961143445    .020297511853228926    .023064808226914157
18321    .019549330341170033 -.002142716311355584 -.0028689604157677824     .02441442443602735    .010398688994791877
18351    -.02178811221601447   .05703899770053844    .10113564274026064  -.0012868477955049057    .005632532115114254
18382  -.0026749459129589426   .01319598637195019    .05879052525623885    .004819294166790847    -.01620245139239078
18412     .03902535249228272  -.04979168018370136    .03822085498507351    .010549351499692272    .025550168542276097
18443    .010370519092942482  .048199239240456465    .13720399387160262    .015963940726483765     -.0130644660396627
18473     .04600416096976601  .011029841399184625   .030069513806459596    .010671871567129822     .02216896538987303
18504     .03233566489935693  .010829973861835443  -.028356607396660984   -.005122831126895943    .014175146734359035
18535   -.007586483195828511   .08142832961465962   .026493448956905082    .010438619405220145    .017403401061928586
18565      .0557251559220764 .0008337146951032286    .04948762600382413     .02104929782605215    .009795087844231075
18596    .037071931567361015  -.04567240868050797   -.12670453210107432  -.0005483262707309533     .07704273653289649
18626    -.03037690948485215  .016616223036462607   .058882451020972074     .03263759169249675   -.014307362493592234
18657    .007514795009862518  -.10733891669444211   -.16609907236377766     .01146289971624094    .052050339474803003
18686    .022041879279909124  -.04701471777306566   -.09050393565310812   -.017593657972898025     .03483216015125226
18716    .007716576704352149   .07077136479502687    .07524872131440774    .025152189473045837    -.00465371915554242
18747    .004377900687003302 -.002295134207331475    .05396326708824468     .01263858498210987    .018705215912480165
18777   -.003496827233725179  -.04897196489146304   -.02402107090457143   -.015240512733154715     .03189004500843755
18808    .002292519646038116  .013335449092528293    .03905907762502119    .016199090424298197     .03656149866142937
18838     .04901408260872691 -.028421368469604805   .000991883229465927   -.009210971326104208    .009350164403755716
18869     .07528509252174581  -.09884788674467222   -.08791482856471086    -.02372926597412328     .06463909979052998
18900    .009377264516429847 -.022793909595720856  -.012122650194557306    .023989252903277657   -.020900227112394806
18930   .0038781484704318303   .05302488060379295    .04154195377201581     .00945019362030132    .002287531574314279
18961     .04450613520536156  -.11057252838530636    -.1897650862340316   .0069532070773171695     .03046417906533587
18991     .03184150434579225  -.06396166402647532    -.1070319258388108    .017567505470008946    .045685403694561144
19022     -.1067921953932999   .09753530254671418    .11676460835167493   -.025638369286820173    -.09537787887008385
19052    -.04301509906045406  .058772026205247026     .3070455329549663    .006528308729473964   -.018348375763223873
19082     .05806527380510167 -.021861623490129975    .08074722235507548    .011918098396665876     .03138498163358063
19113    .056934072813931466  -.01607854122378937    .10256275067921926     .02639640924035069     .03161198554183506
19143     .03741159308567446  -.06991795306912295   .026027079838056875    .006689160666173352    .011738521018555909
19174    -.03342276756980009   .05359918581349932    .04523122943114229    .008740337592505386   -.013737544572640488
19204     .04256730726696051 -.023460627672218178    .12102905667043336    .004873106591381397    .024643735331878892
19235     .06263318146909266  .006989993709272336    .07857907960728933   -.014531398291101021     .04987303506518417
19266    -.04108747339008603   .07153832178946122    .09946779639858551   .0040499912603234045    -.03955561877371175
19296    .030238466137313593 -.024611873128540324  -.027944810897520315   -.001108629027275692    .008966890081646894
19327     .00407863447274448   .04724201290481994   .018902453366939666   -.014773973738321365   -.003449050645425434
19357   -.008735873582003517  .006460435179366955   .012802198706435623    .009644565156753163   -.007412890113319352
19388    .020126022877205604  .005814400212549169    .22987742168980613   .0016304011574133793     .01748200958594774
19417      .0912300551041374  -.07465854799733722   -.06333466635090926    -.03690983310362672     .06262495718247044
19447     .06873697946230511 -.017872400617454206   .030316877221863766   -.055373973512438945     .10112002424330425
19478   -.028360787912422445  .035210373750927254     .0924971944406468    .004835456878866509    -.05529234292741607
19508     .04506518078613986  .020511381033346824    .18761158012394916   -.018864834059442642    .035159744646813845
19539     .07531176730965757  -.06324212280779057   -.08631595469304748   -.013346064604485577     .04855476780192816
19569     .06409478774769009  -.03262351080297927   -.06165424065336029   -.028278786409560563     .09175053604930858
19600   -.041569636128964596  -.05763921166582483   -.07681599214716367    -.02701807956516991   -.008858732746112631
19631  -.0039003604138840298   .04246521644515159     .0625576463495023    .018534767940747973    .008309102211406669
19661     .02980941962626038   .07155093272715338    .25640017443298313   .0046376743111619795   -.031874893883960795
end
format %tdnn/dd/CCYY Date

Thank you.
Priya

[priya sawaliya]

Thank you so much @Shah sir. I understood now. it is great help for me.