Dear Stata members
I am trying learn an index which works on the % of textual counts of certain words to total words. However, ultimately I need to create an index based on the below logic
"The monthly index is the number of articles related to external shocks divided by the total number of articles and is then normalized to have an average value of 100". In another article it is stated as "Subsequently, the authors divided each monthly count by the mean (from 2000 to 2009) of the series and multiplied it by 100 to obtain normalized value index".
In the above dataset, COUNT_USA represents the percent of articles for USA and COUNT_VEN represents percent of articles with Veneuzuela. Based on the above statements can we convert these country figures into normalized values to have an average value of 100.
I am trying learn an index which works on the % of textual counts of certain words to total words. However, ultimately I need to create an index based on the below logic
"The monthly index is the number of articles related to external shocks divided by the total number of articles and is then normalized to have an average value of 100". In another article it is stated as "Subsequently, the authors divided each monthly count by the mean (from 2000 to 2009) of the series and multiplied it by 100 to obtain normalized value index".
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(month COUNT_USA COUNT_VEN COUNT_TWN COUNT_UKR) 480 1.198969 .0269768 .05619781 .02408478 481 1.2321326 .018208856 .13607757 .025514543 482 1.0100458 .01916076 .23037936 .03839656 483 1.0314224 .011993283 .05822659 .04159042 484 1.3923223 .011365897 .09688358 .032294527 485 1.2740495 .02004123 .07413509 .04118616 486 .9578033 .014826676 .0967898 .008065817 487 .820109 .04737091 .031365167 .007841292 488 1.1048642 .03076837 .068083815 .015129738 489 1.7102947 .04124505 .04268944 .014229812 490 .9510832 .02780945 .02993788 .01496894 491 .9818148 .037049614 .031142946 .015571473 492 1.0246317 .03762772 .14675668 .03668917 493 1.1720848 .009477775 .04025117 .0483014 494 1.1910303 .016428005 .1081237 .05766597 495 1.1368091 .02110786 .23651484 .022888534 496 1.2756264 .011389522 .08127078 .02955301 497 1.512445 .01488627 .031407036 .08636934 498 1.317399 .0031366644 .05504876 .023592325 499 1.349414 .01558215 .03089996 .04634994 500 13.22901 .06319591 .09726171 .02244501 501 13.100196 .068948396 .11903928 .09803235 502 7.890069 .04071543 .065198496 .02897711 503 6.047551 .025530556 .068156004 .03786445 504 4.232385 .027403934 .02239642 .02986189 506 3.863307 .031856358 .07072636 .02121791 507 3.840491 .15030675 .01544998 .00772499 508 4.089164 .05356983 .05158058 .014737307 509 3.673885 .037016474 .02295157 .007650524 510 2.954308 .009499382 .04058112 .01623245 511 3.130244 .035497613 .032226875 .032226875 512 4.3860188 .04300018 .03000525 .05250919 513 4.1565065 .06166092 .05547081 .027735405 514 3.697484 .03830063 .02202805 .09545488 515 4.328755 .3329811 .04454012 .02227006 516 4.968297 .27088976 .04305087 .02870058 517 5.824158 .28083614 .04606172 .02303086 518 9.00474 .1912364 .05243839 .05243839 519 6.587385 .08744317 .04202564 .04902991 520 4.000343 .022842785 .0851547 .0283849 521 3.549583 .03917196 .065592885 .021864295 522 3.4111476 .03067579 .00762079 .01524158 523 3.3566434 .03108003 .05148194 .036772817 524 2.818476 .0353045 .00744879 .00744879 525 2.644918 .02965759 .04014721 .033456005 526 2.8708134 .01805543 .05817759 .029088793 527 3.160041 .0127421 .14273909 .02253775 528 2.693432 .023862077 .05884949 .007356186 529 2.816728 .02465407 .029502876 .014751438 530 3.165661 .08007092 .0508167 .04355717 531 3.546311 .017880557 .1105298 .12526712 532 2.880948 .04987385 .06508063 .014462362 533 3.3609934 .05969793 .0227704 .007590133 534 3.1758 .015079772 .03765627 .022593765 535 3.450163 .1033529 .014394702 .035986755 536 3.5085096 .023928454 .00727855 .0291142 537 2.939661 .00858713 .013637913 .013637913 538 2.974993 .036024135 .030355923 .12901267 539 2.5848656 .02222857 .0754774 .1886935 end format %tm month
In the above dataset, COUNT_USA represents the percent of articles for USA and COUNT_VEN represents percent of articles with Veneuzuela. Based on the above statements can we convert these country figures into normalized values to have an average value of 100.

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