wish I could delete a post that seems rather silly now with more understanding.
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can't delete a post?
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Setting up data for competing risk analysis
Hello,
So I used a cox proportional regression model and I am pretty sure the assumptions have been violated mainly due to my Kaplan Meier curve showing a protective effect for the curve with the lowest survival. I researched STATA's stcrreg command which will probably work for me. However, I do not see any information on how to set-up the data first. For example, my data as death and transplant and the competing events but these are two separate variables as shown in my data example below. Do I recode death=2 if a patient had transplant? Or better yet, how do I know which variable is the competing risk variable. Thank you in advance.
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So I used a cox proportional regression model and I am pretty sure the assumptions have been violated mainly due to my Kaplan Meier curve showing a protective effect for the curve with the lowest survival. I researched STATA's stcrreg command which will probably work for me. However, I do not see any information on how to set-up the data first. For example, my data as death and transplant and the competing events but these are two separate variables as shown in my data example below. Do I recode death=2 if a patient had transplant? Or better yet, how do I know which variable is the competing risk variable. Thank you in advance.
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Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float id str1(transplant cancer) float(died time) 1 "Y" "N" 1 2.3 2 "Y" "Y" 1 4.8 3 "Y" "N" 0 7 4 "N" "N" 1 3.3 5 "Y" "Y" 0 7 6 "N" "Y" 1 2.8 7 "Y" "N" 1 2.2 8 "Y" "Y" 0 5 9 "N" "Y" 1 2.5 10 "N" "N" 0 5 end
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Group mean and separating data into 3 groups
Hi, I have a large set of data sets and multiple variables. I want to do the following:
1. First I want to divide my variables into three groups (pre, post and between) based on year. For example, If the year is less than in 1990 it is pre_period. If the year is more than in 1995 it is post_period and if the year is between 1990 and 1995 then this group is "between". I don't know how to do that in STATA.
2. I want to find mean difference of many variables such as ROA, ROE, TA, CASH, R&D, CAPX and many others variables between pre and post period. I can do ttest for each variable separately but is there any way I can do them all together and find the mean difference in one table? There is another problem. Since I have three groups of data in my data set how can I do that pre post difference?
3. I want to get the output of mean difference in an excel table with significant result. Can I do that with Outreg2? if so,
how is that?
Can anyone please help me?
1. First I want to divide my variables into three groups (pre, post and between) based on year. For example, If the year is less than in 1990 it is pre_period. If the year is more than in 1995 it is post_period and if the year is between 1990 and 1995 then this group is "between". I don't know how to do that in STATA.
2. I want to find mean difference of many variables such as ROA, ROE, TA, CASH, R&D, CAPX and many others variables between pre and post period. I can do ttest for each variable separately but is there any way I can do them all together and find the mean difference in one table? There is another problem. Since I have three groups of data in my data set how can I do that pre post difference?
3. I want to get the output of mean difference in an excel table with significant result. Can I do that with Outreg2? if so,
how is that?
Can anyone please help me?
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calculating the growth rate of a phenomenon from its columns
I'm trying to calculate the transmission rate of the virus by country as new column variables. I want to subtract the first reported number of cases (in the total_cases column) from the latest number of cases (in the total_cases column) and divide the answer by the number of days that have passed since the first case was reported by that country. I also want to calculate the first 15 days growth rate of the virus by subtracting the first reported cases from the number of total cases on the 15th day divided by 15. The new variables to be named fifteen_day_rate and average_rate.
I appreciate any help.
. dataex
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I appreciate any help.
. dataex
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Code:
* Example generated by -dataex-. To install: ssc install dataex clear input str10 date str32 location int(new_cases new_deaths) long total_cases int total_deaths "12/31/2019" "Afghanistan" 0 0 0 0 "1/1/2020" "Afghanistan" 0 0 0 0 "1/2/2020" "Afghanistan" 0 0 0 0 "1/3/2020" "Afghanistan" 0 0 0 0 "1/4/2020" "Afghanistan" 0 0 0 0 "1/5/2020" "Afghanistan" 0 0 0 0 "1/6/2020" "Afghanistan" 0 0 0 0 "1/7/2020" "Afghanistan" 0 0 0 0 "1/8/2020" "Afghanistan" 0 0 0 0 "1/9/2020" "Afghanistan" 0 0 0 0 "1/10/2020" "Afghanistan" 0 0 0 0 "1/11/2020" "Afghanistan" 0 0 0 0 "1/12/2020" "Afghanistan" 0 0 0 0 "1/13/2020" "Afghanistan" 0 0 0 0 "1/14/2020" "Afghanistan" 0 0 0 0 "1/15/2020" "Afghanistan" 0 0 0 0 "1/16/2020" "Afghanistan" 0 0 0 0 "1/17/2020" "Afghanistan" 0 0 0 0 "1/18/2020" "Afghanistan" 0 0 0 0 "1/19/2020" "Afghanistan" 0 0 0 0 "1/20/2020" "Afghanistan" 0 0 0 0 "1/21/2020" "Afghanistan" 0 0 0 0 "1/22/2020" "Afghanistan" 0 0 0 0 "1/23/2020" "Afghanistan" 0 0 0 0 "1/24/2020" "Afghanistan" 0 0 0 0 "1/25/2020" "Afghanistan" 0 0 0 0 "1/26/2020" "Afghanistan" 0 0 0 0 "1/27/2020" "Afghanistan" 0 0 0 0 "1/28/2020" "Afghanistan" 0 0 0 0 "1/29/2020" "Afghanistan" 0 0 0 0 "1/30/2020" "Afghanistan" 0 0 0 0 "1/31/2020" "Afghanistan" 0 0 0 0 "2/1/2020" "Afghanistan" 0 0 0 0 "2/2/2020" "Afghanistan" 0 0 0 0 "2/3/2020" "Afghanistan" 0 0 0 0 "2/4/2020" "Afghanistan" 0 0 0 0 "2/5/2020" "Afghanistan" 0 0 0 0 "2/6/2020" "Afghanistan" 0 0 0 0 "2/7/2020" "Afghanistan" 0 0 0 0 "2/8/2020" "Afghanistan" 0 0 0 0 "2/9/2020" "Afghanistan" 0 0 0 0 "2/10/2020" "Afghanistan" 0 0 0 0 "2/11/2020" "Afghanistan" 0 0 0 0 "2/12/2020" "Afghanistan" 0 0 0 0 "2/13/2020" "Afghanistan" 0 0 0 0 "2/14/2020" "Afghanistan" 0 0 0 0 "2/15/2020" "Afghanistan" 0 0 0 0 "2/16/2020" "Afghanistan" 0 0 0 0 "2/17/2020" "Afghanistan" 0 0 0 0 "2/18/2020" "Afghanistan" 0 0 0 0 "2/19/2020" "Afghanistan" 0 0 0 0 "2/20/2020" "Afghanistan" 0 0 0 0 "2/21/2020" "Afghanistan" 0 0 0 0 "2/22/2020" "Afghanistan" 0 0 0 0 "2/23/2020" "Afghanistan" 0 0 0 0 "2/24/2020" "Afghanistan" 0 0 0 0 "2/25/2020" "Afghanistan" 1 0 1 0 "2/26/2020" "Afghanistan" 0 0 1 0 "2/27/2020" "Afghanistan" 0 0 1 0 "2/28/2020" "Afghanistan" 0 0 1 0 "2/29/2020" "Afghanistan" 0 0 1 0 "3/1/2020" "Afghanistan" 0 0 1 0 "3/2/2020" "Afghanistan" 0 0 1 0 "3/8/2020" "Afghanistan" 3 0 4 0 "3/11/2020" "Afghanistan" 3 0 7 0 "3/15/2020" "Afghanistan" 3 0 10 0 "3/16/2020" "Afghanistan" 6 0 16 0 "3/17/2020" "Afghanistan" 5 0 21 0 "3/18/2020" "Afghanistan" 1 0 22 0 "3/19/2020" "Afghanistan" 0 0 22 0 "3/20/2020" "Afghanistan" 0 0 22 0 "3/21/2020" "Afghanistan" 2 0 24 0 "3/9/2020" "Albania" 2 0 2 0 "3/10/2020" "Albania" 4 0 6 0 "3/11/2020" "Albania" 4 0 10 0 "3/12/2020" "Albania" 1 1 11 1 "3/13/2020" "Albania" 12 0 23 1 "3/14/2020" "Albania" 10 0 33 1 "3/15/2020" "Albania" 5 0 38 1 "3/16/2020" "Albania" 4 0 42 1 "3/17/2020" "Albania" 9 0 51 1 "3/18/2020" "Albania" 4 0 55 1 "3/19/2020" "Albania" 4 1 59 2 "3/20/2020" "Albania" 11 0 70 2 "3/21/2020" "Albania" 0 0 70 2 "12/31/2019" "Algeria" 0 0 0 0 "1/1/2020" "Algeria" 0 0 0 0 "1/2/2020" "Algeria" 0 0 0 0 "1/3/2020" "Algeria" 0 0 0 0 "1/4/2020" "Algeria" 0 0 0 0 "1/5/2020" "Algeria" 0 0 0 0 "1/6/2020" "Algeria" 0 0 0 0 "1/7/2020" "Algeria" 0 0 0 0 "1/8/2020" "Algeria" 0 0 0 0 "1/9/2020" "Algeria" 0 0 0 0 "1/10/2020" "Algeria" 0 0 0 0 "1/11/2020" "Algeria" 0 0 0 0 "1/12/2020" "Algeria" 0 0 0 0 "1/13/2020" "Algeria" 0 0 0 0 "1/14/2020" "Algeria" 0 0 0 0 end
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IRA syntax
halo everyone, if I want to adjust the null distribution to perform rWG (interrater agreement).
I have installed ira module and in the help menu, the syntax is:
ira judge_id rating [if] [in] [, item(item_id) group(group_id) options(#) distribution(#)]
question: what # in the option distribution(#) means? Is it the correct option to change the null distribution?
many thanks.
Andy
I have installed ira module and in the help menu, the syntax is:
ira judge_id rating [if] [in] [, item(item_id) group(group_id) options(#) distribution(#)]
question: what # in the option distribution(#) means? Is it the correct option to change the null distribution?
many thanks.
Andy
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problem with reshape, always show that "variable demacity-2001 implied name too long"
I have tried many times without success with "reshape long demacity- demanation, i(citycode_s ) j(year)"
It always show that "variable demacity-2001 implied name too long",why
many thanks for kindly guys.
It always show that "variable demacity-2001 implied name too long",why
many thanks for kindly guys.
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double(citycode_s demacity2001 demacity2005 demacity2010 undemacity2001 undemacity2005 undemacity2010 demanation2001 demanation2005 demanation2010) 1100 .02000062486995376 .023090715008419284 .15928207577014655 -.007811453489032042 -.01358161624039658 -.13161947652104344 .2731945083666961 -.05749517095397304 -.10393346104293384 1200 -.029857292390218945 .0757122451955909 -.05744057014209189 .007104473758962886 -.025690480700915157 .029042912691038456 2.7500534257666436 -.7075350428603924 1.7947110727435598 1301 .08379923726205893 .0008584498362568188 .08075491764893876 -.04868597927287028 .011072762047219041 -.05762105287691779 -.5271144885258346 .9964305939443858 -.49038291795365513 1302 -.05057238284012947 .2860610289675229 .012871507155237267 .007662728374994464 -.06010311138492896 -.0032140161871804934 -.5094631375149119 .8426107942848683 -.3049178568955304 1303 -.10805056916984486 .2010230105290625 .12276622935587785 -.012875115884429278 -.055846519637822584 -.017777704865286327 -.6153040296507275 1.192276674260052 -.20292503217799773 1304 1.0127051573458776 .6897040121830953 -.11805662148488354 -.19834821180920859 -.10663056566220698 .06480792031341477 .9114140105940657 .3009844979384577 -.3969816918598409 1305 -.1089480666293561 .17782920261076351 .39054458292417593 .006339195570080058 -.028826861200505913 -.052238071036653996 -.6407826921559593 1.5863971488505237 -.3324247218088402 1306 .1463818341662537 .08170956337788184 .0983672775747338 -.027846625099879285 -.022769472341683715 -.014407733413532802 -.4801019148340075 .8854113183399331 -.487791307264141 1307 -.13275622958008262 .34299327768232696 .08552504171343879 -.0006103804855940101 .07408745619352258 -.07337867040522193 -.6605236976366567 1.0582170399564363 -.45490648002667977 1308 .6028291126545572 -.10197759122351742 -.07275508054786738 -.1089917171148209 .032284431496008965 .05357939784526251 -.41604836393040295 1.1903085269131424 -.4263311144998655 1309 .23040351300308498 -.06469723236414318 .015417966573802733 -.03655705572778736 .017425662190456864 -.01535289098941449 -.3852103875457466 .6303422743210922 -.283054498361367 1310 -.19014470987938345 .2254634187931466 .08291521902945098 .031079894575829128 -.0823723257682709 .006892191125993297 -.480214153209058 .5456884961920154 -.1877219135195837 1401 .06332764950483495 .04823396078272028 .09302314701254047 -.030193777708176633 -.01936035858571311 -.051869660478807776 .2459513902482437 -.2614138150248429 .4351273177227912 1402 -.16469638054229332 .05777308835209067 -.10796339766057107 .03980617462911336 -.020508903068674453 .04928288650020114 .03392662158540894 -.2900423210530056 .41537771328098677 1403 -.18927436942142825 .3404697191692713 -.08361446231230849 .024317601774671536 -.02265561507563996 -.017766128535631455 .4894345761700162 -.2816525467822523 .6563927512745429 1404 .06257041042671134 -.024577715431320608 .027780337173617487 -.03547596559005393 .039665750879058125 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-.4205197434195724 3203 -.07645803039860734 .2881304562940337 .024720449670016388 -.00048181925437554247 -.044243946164686104 -.0016498417255543458 -.47479596011006653 1.0540804002706152 -.43883896302702485 3204 .15570676309712828 -.0033864798354466363 .2157626479996092 -.02748099334059942 -.003961391195696417 -.06342411733856063 -.24171297891406038 .5866767309871647 -.2312660451533521 3205 .016600518099218604 .17005783951538678 .06009994471375663 -.00889205461131339 -.02675999546101847 -.021841037126642756 -.3707573669598044 2.016750157933332 -.5446682953879288 3206 .03688614672975652 .19113557159673875 .050729215198249175 -.008078262512474677 -.0353877747524597 -.009792745205105114 -.4242029811720421 1.0029277790578692 -.42475987603310994 3207 -.11166234597394017 -.10550727054028068 .11072095827706178 .029654741321624937 .035372021526281876 -.03463310187471895 -.5943543979897187 1.5447203832819665 -.028600764826229735 3208 -.18661681650257192 .23383333205589335 .12829380855336395 .030272517503922663 -.03890617235180688 .01973746204936653 -.23750906177341363 .9086079350083498 -.2672464323770287 3209 -.03140157002986028 .21451604054360648 .11547667683804826 -.011773010540158113 -.06597289381004087 .050314262962637066 -.4887300971416917 1.0633022113552537 -.38321307073574196 3210 -.04552137867202128 .10284149219129057 .08366324664658253 .0015296927631849916 -.021431579466192775 .005459119036306447 -.45065614749353483 .8196492143585014 -.22001765399703646 3211 -.1161565569230396 .36062902042340367 .06553666535492018 .0326949613910514 -.056901315416588834 -.033928031586189886 -.5528171096730713 1.5624453143935055 .009198152258380037 3212 .1315574979807891 -.1299911855262449 .31028838070639514 -.024563259209614524 .02829296512394182 .015678782367924003 -.5464093922022403 1.0018073980571107 -.1168437320074287 3213 -.14796296296296285 .12359980223973638 .3214203521283588 .04500354551900948 -.08648463411129348 .0033109526984537105 -.4134710268225992 2.0605488558145377 -.06085308762022189 3301 .15853745039360667 .0798215620259606 .15051725077075895 -.04270147192685208 -.02612911939124458 -.07296919310511711 -.379774020337235 1.1435207359950195 -.411601520580462 3302 -.15236550572832458 .2688478988241093 .2344290168868603 .01324369615461344 -.034585686790110225 -.06545401569508472 -.0976557475141692 .7692517167422326 -.42113039155939724 3303 -.07904560204312956 -.12438613387654163 .41653945855022195 -.009381510167915058 .008486888218488479 -.05729654428495266 -.37293518406120013 1.221978499212342 -.3241805994138636 3304 -.47731202506266146 1.481259192334287 -.04866213257270795 .02131702744797536 -.07086081661134311 .031234368754565416 -.5038995192036884 2.7319691949807012 -.24765317138130483 3305 -.5529666773874632 .6499457677548337 .7590956218060065 .08363860067076254 -.02890450154526661 -.10623284000594939 -.471965928167877 .6579219585501799 .1530901686418902 3306 -.1380239059107851 .8450819011235894 -.046613775396677264 -.007253289648917072 -.07318132417441903 .01795533914014305 -.38610629532238144 1.264074145497094 -.15896165611592045 3307 -.03744781127956727 .40398119554011847 .011788285742606562 -.0055877498389332895 -.03827345280379941 -.003675270392434649 -.31783876557815177 1.2577549091640674 -.3206752958326537 3308 -.016816691400689615 .12640784109791814 -.17365915708040927 -.015911779735460752 -.03871381166187329 .07202434946012434 -.5337410143980297 1.5176758394527399 .03284577163043404 3309 -.018415619295151484 -.05152716094214396 .038948397295259916 -.032309428985245574 .033034628752860035 .04254398477861629 -.3378841179609802 .638790134735182 .1530888288177572 3310 .1624869341902601 .3364068668532811 .10577206158448169 -.023323857445409792 -.03266808901695987 -.025036836441388044 -.3674565680831082 1.1308137301999581 -.4153568076625816 3311 -.25925230619301265 .3813631052057739 -.07475966049143305 .020790307759399405 -.1308275816387992 .06537379446820472 -.3923822634106867 1.682023545667085 -.060413568665745196 3401 -.20728335480852228 .13853780920525285 .048176011652270576 .02546539389371965 -.046802365904316946 -.023627059300970657 -.46026761720671094 1.2164949806047056 -.3265011039203393 3402 .3771713634054329 -.21839307268399338 .044513474668263664 -.09260399406814425 .07781879118407614 -.028307256590414054 .06663208616044577 .28194886993059864 .03713094461285684 3403 -.09026691142775473 .05274145036396479 .08639204076344353 .01735470363330154 -.02604170704044735 .012110822928997704 -.5910955797346592 .740414053552357 -.15575149387405307 3404 -.3115784463089991 .6725443001024841 -.16585852657554537 .008594449809501395 -.05906665714188387 .06768344976046801 -.5669766557253744 .7900702827374111 -.1511878578296826 3405 -.3918425193206534 .7519031941315774 .03768728831540035 .030489980383687824 -.0950646313327644 -.02063943356299323 -.7084876584290236 1.4225055255626027 .47769874163144604 3406 -.11269906025943877 .3033926072039465 .019337939722094254 -.01734627391285347 -.08144496880907337 -.06129298508972363 -.4147945425578703 .5450145369397685 .505951589496288 3407 .09903291204509365 -.041205660252821304 -.07575845299305328 -.038147172708198764 .0317623857104059 .10313158535355896 -.2212708981866046 .02613188635722894 .7249935836946187 3408 .2597801876122031 -.003818312830519474 .024425212680899193 -.033639546787281656 .003186661392083842 -.016955464163133496 -.4097165655849221 .8887904281885273 .39444317347672914 3410 .04495335029686175 -.19894282550557846 .16120597899023548 -.13749453825789698 .1450472641066012 -.13035279642656547 -.37644760326655063 .5946424627798256 1.403302432234995 3411 -.3065340436222492 .9583321319465054 -.11496614644978234 .021784563566518013 -.15400672424399023 .05456068707773957 -.5882293725591072 1.749528765910974 -.21172660584030753 3412 .012434768714326284 .19789026745243005 .28980151077528027 -.017217457513584767 -.03721161359690956 .08778367084357096 -.3117363146336762 .06014537415730524 -.27851275363058353 3413 -.10209399685083584 .2000166719176644 .11583530945348146 .009909579326912854 -.07409323305518961 .3903262857531627 -.4976199325464344 1.409217045885116 -.1796418958668094 end
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Simulteneously executing multiple do files
Suppose I have three do files with following commands in them
File diX.do : di "X"
File diY.do : di "Y"
File diZ.do : di "Z"
How do I execute them simultaneously in separate instances of Stata?
I created a do file as follows
do diX.do
do diY.do
do diZ.do
but it executes them in a single instance one after another.
I looked into package parallel but could not find the install package, I looked into multishell package which seemed to be geared toward running simulations, I looked into batch processing but that requires entering commands separately. I tried winexec command but that simply open the do file but does not execute it. What am I missing? I am sure there is a way.
File diX.do : di "X"
File diY.do : di "Y"
File diZ.do : di "Z"
How do I execute them simultaneously in separate instances of Stata?
I created a do file as follows
do diX.do
do diY.do
do diZ.do
but it executes them in a single instance one after another.
I looked into package parallel but could not find the install package, I looked into multishell package which seemed to be geared toward running simulations, I looked into batch processing but that requires entering commands separately. I tried winexec command but that simply open the do file but does not execute it. What am I missing? I am sure there is a way.
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Store Coefficient after loop regressions
Dear all,
For our master paper, we need to replicate the Fama and French three factor model. We started with the one factor model to try the different regressions. We are struggling to do certain regressions and afterwards extract the different coefficients.
At the moment, we have 520 variables. We have one dependent variable: MktRF (Fama and French one factor model) and 519 independent variables (companies) with the daily excess returns over 20 years.
We were able to do our loop regression (for our 519 firms: from the variable SOLVAY to ADDECOGROUP), by executing the following command:
foreach x of varlist SOLVAY-ADDECOGROUP{
regress `x' MktRF
}
The problem now is that we need to list all the coefficients of MktRF and _cons and generate these coefficients as 2 different variables called ‘coefficientMktRF’ and ‘coefficient_cons’.
We were able to use the Statsby command, but this command only lists the two coefficients of our last regression (our last firm) and not of our 519 regressions.
Is there anyone who can help us to proceed our master thesis?
For our master paper, we need to replicate the Fama and French three factor model. We started with the one factor model to try the different regressions. We are struggling to do certain regressions and afterwards extract the different coefficients.
At the moment, we have 520 variables. We have one dependent variable: MktRF (Fama and French one factor model) and 519 independent variables (companies) with the daily excess returns over 20 years.
We were able to do our loop regression (for our 519 firms: from the variable SOLVAY to ADDECOGROUP), by executing the following command:
foreach x of varlist SOLVAY-ADDECOGROUP{
regress `x' MktRF
}
The problem now is that we need to list all the coefficients of MktRF and _cons and generate these coefficients as 2 different variables called ‘coefficientMktRF’ and ‘coefficient_cons’.
We were able to use the Statsby command, but this command only lists the two coefficients of our last regression (our last firm) and not of our 519 regressions.
Is there anyone who can help us to proceed our master thesis?
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SIR Model
Hi everyone!! Is there any way to fit SIR Models in Stata?
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How do i open one of these .dat files?
https://data.nber.org/data/cps_basic.html for example March 2015 how would i open this in Stat? Thanks
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define constraints for baseoutcome in mlogit
I want to estimate a mlogit model
there are categories in my factor variable fvar1 that do not occur for certain alternatives in depvar.
if i
I wrote now a script to define constraints for the deterministic outcomes. In this example they would give
when i estimate the model now with
then the constraints are correctly applied, however the constraints defined to the base-outcome are ignored and are set to 0 by stata automatically.
so it adds the constraint
but in the end ignores my constraint numer 3 (constraint define 3 [Alt2]3.group = -99999)
and estimates the model internally with constraints 1 2 4/6 1997/1999
I got estimates for the coefficients.
Stata returns for example coefficients like 20 and 19 for group3 in the equations for Alt3 and Alt4 (and uses 0 for Alt2 internally), so that the prediction results in reasonable values (10E-6 probability for Alt2 and 0.6 and 0.4 for Alt3 and Alt4). but the standard errors and z-values for the coefficients are not useful.
I could define another baseoutcome with
but that does not help, because in my dataset for all alternatives there is at least one "0" in the table above.
Is it somehoe possible not to define a baseoutcome at all for mlogit (or other similar commands) and to define all constraints for the baseoutcome manually?
So in this case:
keep the -99999 as a constraint for [Alt2]1.group
and define manually a different baseoutcome for group 3 (taking Alt3 instead of Alt2)
So using the following constraints:
Any help and idea is apreciated,
I hope you all stay healthy these days,
Max
Code:
mlogit depvar ibn.fvar1 othervars
if i
Code:
tab depvar fvar1
| Alt1 | Alt2 | Alt3 | Alt4 | |
| Group1 | 100 | 80 | 0 | 0 |
| Group2 | 0 | 50 | 40 | 0 |
| Group3 | 0 | 0 | 60 | 40 |
Code:
constraint define 1 [Alt1]2.group = -99999 constraint define 2 [Alt1]3.group = -99999 constraint define 3 [Alt2]3.group = -99999 constraint define 4 [Alt3]1.group = -99999 constraint define 5 [Alt4]1.group = -99999 constraint define 6 [Alt4]2.group = -99999
Code:
mlogit depvar ibn.fvar othervariables, constraint(1/6)
so it adds the constraint
Code:
constraint define 1997 [Alt2]1o.group = 0 constraint define 1998 [Alt2]2o.group = 0 constraint define 1999 [Alt2]3o.group = 0
and estimates the model internally with constraints 1 2 4/6 1997/1999
I got estimates for the coefficients.
Stata returns for example coefficients like 20 and 19 for group3 in the equations for Alt3 and Alt4 (and uses 0 for Alt2 internally), so that the prediction results in reasonable values (10E-6 probability for Alt2 and 0.6 and 0.4 for Alt3 and Alt4). but the standard errors and z-values for the coefficients are not useful.
I could define another baseoutcome with
Code:
mlogit ibn.fvar othervariables, constraint(1/6) baseoutcome(1)
Is it somehoe possible not to define a baseoutcome at all for mlogit (or other similar commands) and to define all constraints for the baseoutcome manually?
So in this case:
keep the -99999 as a constraint for [Alt2]1.group
and define manually a different baseoutcome for group 3 (taking Alt3 instead of Alt2)
So using the following constraints:
Code:
constraint define 1 [Alt1]2.group = -99999 constraint define 2 [Alt1]3.group = -99999 constraint define 3 [Alt2]3.group = -99999 constraint define 4 [Alt3]1.group = -99999 constraint define 5 [Alt4]1.group = -99999 constraint define 6 [Alt4]2.group = -99999 constraint define 1997 [Alt2]1o.group = 0 constraint define 1998 [Alt2]2o.group = 0 constraint define 1999 [Alt3]3o.group = 0
I hope you all stay healthy these days,
Max
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Cleaning data of Globar Entrepreneurship Monitor
Dear All,
I am currently dealing with cross-sectional studies derived from the Global Entrepreneurship Monitor (GEM) from 2004 to 2016. This study investigates the entrepreneurial activity across countries based on individual surveys. I am using a multi-level research design as I take the individual-level data from GEM and merge them with country-level factors such as corruption, the competitiveness of a country, etc. I investigate the country-level factors' impact on certain individual-level factors. My question relates to the cleaning phase.
After I selected my variables from GEM and appended the separate datasets from 2004 to 2016, I arrived at more than 2 million observations. In my initial dataset for the individual factors, I have around 16 variables and most of the observations are not complete, implying missing values. If I only keep the complete observations, I am left with approximately 9 000 observations. However, as I mentioned above, I would like to merge the individual-level data with country-level variables to obtain country-level effects. As a result of deleting the incomplete observations, some countries dropped out of certain years in the observations. For example, Germany is not presented in 2006 and 2007. Indeed, only a few countries (approx. 5) have complete data in all the examined years (2004-2016). This arose the question of whether I can still investigate differences between country despite the fact that not all the countries are completly presented in the dataset. Do you have anny suggestions how to overcome this issue?
This is the -datex- before dropping out the missing values:
This is the -datex- after dropping out the missing values:
Thank you so much for your help and time!
I am currently dealing with cross-sectional studies derived from the Global Entrepreneurship Monitor (GEM) from 2004 to 2016. This study investigates the entrepreneurial activity across countries based on individual surveys. I am using a multi-level research design as I take the individual-level data from GEM and merge them with country-level factors such as corruption, the competitiveness of a country, etc. I investigate the country-level factors' impact on certain individual-level factors. My question relates to the cleaning phase.
After I selected my variables from GEM and appended the separate datasets from 2004 to 2016, I arrived at more than 2 million observations. In my initial dataset for the individual factors, I have around 16 variables and most of the observations are not complete, implying missing values. If I only keep the complete observations, I am left with approximately 9 000 observations. However, as I mentioned above, I would like to merge the individual-level data with country-level variables to obtain country-level effects. As a result of deleting the incomplete observations, some countries dropped out of certain years in the observations. For example, Germany is not presented in 2006 and 2007. Indeed, only a few countries (approx. 5) have complete data in all the examined years (2004-2016). This arose the question of whether I can still investigate differences between country despite the fact that not all the countries are completly presented in the dataset. Do you have anny suggestions how to overcome this issue?
This is the -datex- before dropping out the missing values:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double(country yrsurv gemhhinc gemeduc omexport omnowjob gender age estbbuso knowenyy suskilyy frfailyy teayyopp teayynec eb_cust eb_tech eb_yytec eb_jobgr) 1 2016 33 1212 . . 2 63 0 0 1 1 0 0 . . -2 . 1 2016 68100 1316 . . 1 52 0 0 1 1 0 0 . . -2 . 1 2016 3467 1316 . . 2 64 0 0 0 0 0 0 . . -2 . 1 2016 3467 1316 6 2 2 70 1 0 0 0 0 0 2 3 0 0 1 2016 3467 1316 . . 1 -2 0 0 0 1 0 0 . . -2 . end label values country country label def country 1 "United States", modify label values gemhhinc GEMHHINC label def GEMHHINC 33 "Lowest 33%tile", modify label def GEMHHINC 3467 "Middle 33%tile", modify label def GEMHHINC 68100 "Upper 33%tile", modify label values gemeduc GEMEDUC label def GEMEDUC 1212 "SECONDARY DEGREE", modify label def GEMEDUC 1316 "POST SECONDARY", modify label values omexport omexport label def omexport 6 "10% or less", modify label values omnowjob omnowjob label values gender gender label def gender 1 "Male", modify label def gender 2 "Female", modify label values age age label def age -2 "Refused", modify label values estbbuso ESTBBUSO label def ESTBBUSO 0 "No", modify label def ESTBBUSO 1 "Yes", modify label values knowenyy KNOWENyy label def KNOWENyy 0 "No", modify label values suskilyy SUSKILyy label def SUSKILyy 0 "No", modify label def SUSKILyy 1 "Yes", modify label values frfailyy FRFAILyy label def FRFAILyy 0 "No", modify label def FRFAILyy 1 "Yes", modify label values teayyopp TEAyyOPP label def TEAyyOPP 0 "No", modify label values teayynec TEAyyNEC label def TEAyyNEC 0 "No", modify label values eb_cust EB_CUST label def EB_CUST 2 "Some", modify label values eb_tech EB_TECH label def EB_TECH 3 "No new technology (more than 5 years)", modify label values eb_yytec EB_yyTEC label def EB_yyTEC 0 "No/low technology sector", modify
This is the -datex- after dropping out the missing values:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double(country yrsurv gemhhinc gemeduc omexport omnowjob gender age estbbuso knowenyy suskilyy frfailyy teayyopp teayynec eb_cust eb_tech eb_yytec eb_jobgr) 1 2016 68100 1212 6 1 2 36 1 0 1 0 1 0 3 3 0 0 1 2016 68100 1316 6 3 2 64 1 0 1 0 1 0 3 3 0 0 1 2016 68100 1316 6 0 2 59 1 1 1 0 1 0 2 3 0 0 1 2016 3467 1720 5 0 1 55 1 1 1 1 1 0 3 3 0 0 1 2016 68100 1316 6 24 1 66 1 1 1 0 1 0 3 3 0 6 end label values country country label def country 1 "United States", modify label values gemhhinc GEMHHINC label def GEMHHINC 3467 "Middle 33%tile", modify label def GEMHHINC 68100 "Upper 33%tile", modify label values gemeduc GEMEDUC label def GEMEDUC 1212 "SECONDARY DEGREE", modify label def GEMEDUC 1316 "POST SECONDARY", modify label def GEMEDUC 1720 "GRAD EXP", modify label values omexport omexport label def omexport 5 "11 to 25%", modify label def omexport 6 "10% or less", modify label values omnowjob omnowjob label values gender gender label def gender 1 "Male", modify label def gender 2 "Female", modify label values age age label values estbbuso ESTBBUSO label def ESTBBUSO 1 "Yes", modify label values knowenyy KNOWENyy label def KNOWENyy 0 "No", modify label def KNOWENyy 1 "Yes", modify label values suskilyy SUSKILyy label def SUSKILyy 1 "Yes", modify label values frfailyy FRFAILyy label def FRFAILyy 0 "No", modify label def FRFAILyy 1 "Yes", modify label values teayyopp TEAyyOPP label def TEAyyOPP 1 "Yes", modify label values teayynec TEAyyNEC label def TEAyyNEC 0 "No", modify label values eb_cust EB_CUST label def EB_CUST 2 "Some", modify label def EB_CUST 3 "None", modify label values eb_tech EB_TECH label def EB_TECH 3 "No new technology (more than 5 years)", modify label values eb_yytec EB_yyTEC label def EB_yyTEC 0 "No/low technology sector", modify
Thank you so much for your help and time!
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Sample selection for a panel: struggling with panel bootstrap in the program
Dear Statalisters,
I am using Stata 15.1 and I would like to address the sample selection problem in a panel data set. The approach seems not complicated (as explained in the textbook of Wooldridge: Econometric Analysis of Cross Section and Panel Data. ch. 19. You can also check: Wooldridge, J. M. (1995), ‘‘Selection Corrections for Panel Data Models under Conditional Mean Independence Assumptions,’’ Journal of Econometrics 68, 115-132.).
Very briefly: I have some firms that innovate or not (y1=1 or y1=0), and those that innovate will report their sales from innovations (y2), and thus will show a missing for those firms reporting y1=0. For accounting for sample selection I must to do first a probit model for y1 with respect to the independent variables in the second stage plus some exclusion restrictions (for each year as Wooldridge advice) and then calculate the yearly inverse Mill`s ratios and include them into the second stage. Since these ratios are generated in a previous step, the standard error must to be corrected in the second stage. However, since I am working with a panel data, I cannot just put "bootstrap" before the regression, but to do it in a program.
After two days trying by my self, I cannot understand what am I doing wrong or even how to continue. I will show you the program I created following the guide of Professor Clyde Schechter in post #2:
as well as a short example (dataex) for if it is helpful to see the kind of dataset I am working with.
From the program I need the table (with correction for the standard errors) from the second stage (do not know how to do it). And also to do some Wald tests for the IMR`year' to check if they are jointly zero (also do not know how to do it).
This is the process I want to implement in the program:
And this is what I try to build (of course it is wrong but do not know why)
After several tries with a more simple program, I realized that the condition
in the main equation is problematic (do not why). And also, the fact that the variable y2 present missings for when y1==0 is also problematic (the most simple program does not run, as it did without the missings).
Please, I am very sorry for posting this long post, any help will be much much appreciated.
Here you have a descriptive of the data (I just realize that for the dataex below, if you run the first code --what I want to implement-- will not run properly with such small amount of data)
I am using Stata 15.1 and I would like to address the sample selection problem in a panel data set. The approach seems not complicated (as explained in the textbook of Wooldridge: Econometric Analysis of Cross Section and Panel Data. ch. 19. You can also check: Wooldridge, J. M. (1995), ‘‘Selection Corrections for Panel Data Models under Conditional Mean Independence Assumptions,’’ Journal of Econometrics 68, 115-132.).
Very briefly: I have some firms that innovate or not (y1=1 or y1=0), and those that innovate will report their sales from innovations (y2), and thus will show a missing for those firms reporting y1=0. For accounting for sample selection I must to do first a probit model for y1 with respect to the independent variables in the second stage plus some exclusion restrictions (for each year as Wooldridge advice) and then calculate the yearly inverse Mill`s ratios and include them into the second stage. Since these ratios are generated in a previous step, the standard error must to be corrected in the second stage. However, since I am working with a panel data, I cannot just put "bootstrap" before the regression, but to do it in a program.
After two days trying by my self, I cannot understand what am I doing wrong or even how to continue. I will show you the program I created following the guide of Professor Clyde Schechter in post #2:
HTML Code:
https://www.statalist.org/forums/forum/general-stata-discussion/general/1477399-boostrap-for-xtqreg
From the program I need the table (with correction for the standard errors) from the second stage (do not know how to do it). And also to do some Wald tests for the IMR`year' to check if they are jointly zero (also do not know how to do it).
This is the process I want to implement in the program:
Code:
forvalues year=2005/2015 {
probit y1 main1 main2 x1 x2 x3 z1 z2 z3 if year==`year' /* selection equation */
predict acltxb1_`year' , xb
predict acltpr1_`year', pr
gen acltndenxb1_`year' = normalden(acltxb1_`year')
gen acltnxb1_`year' = normprob(acltxb1_`year')
gen acltlambda1`year' = acltndenxb1_`year' / acltnxb1_`year'
}
forvalues i = 2005/2015 {
gen year`i' = year==`i'
}
forvalues i = 2005/2015 {
generate IMR`i' = acltlambda1`i'*year`i' /* generating IMR*time dumies */
}
xtreg y2 main1 main2 x1 x2 x3 i.year IMR2005-IMR2015 if y1==1, fe /*main equation */
test IMR2005 IMR2006 IMR2007 IMR2008 IMR2009 IMR2010 IMR2011 IMR2012 IMR2013 IMR2014 IMR2015
Code:
xtset, clear
capture program drop xtq_diff
program define xtq_diff, rclass
xtset id
forvalues year=2005/2015 {
probit y1 main1 main2 x1 x2 x3 z1 z2 z3 if year==`year' /* selection equation */
local predict acltxb1_`year' , xb
local predict acltpr1_`year', pr
local gen acltndenxb1_`year' = normalden(acltxb1_`year')
local gen acltnxb1_`year' = normprob(acltxb1_`year')
local gen IMR`year' = acltndenxb1_`year' / acltnxb1_`year'
}
xtreg y2 main1 main2 x1 x2 x3 i.year `IMR2005' `IMR2006' `IMR2007' `IMR2008' `IMR2009' `IMR2010' `IMR2011' `IMR2012' `IMR2013' `IMR2014' `IMR2015' if y1==1, fe /*main equation */
return scalar diff = `IMR2005'-`IMR2006'-`IMR2007'-`IMR2008'
exit
end
bootstrap diff = r(diff), reps(50) seed(10101) cluster(id) idcluster(newid): xtq_diff
Code:
if y1==1
Please, I am very sorry for posting this long post, any help will be much much appreciated.
Here you have a descriptive of the data (I just realize that for the dataex below, if you run the first code --what I want to implement-- will not run properly with such small amount of data)
Code:
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
y1 | 117,559 .4539168 .4978739 0 1
y2 | 53,720 16.03345 27.13275 0 100
main1 | 117,555 177.5277 3387.195 0 513079.3
main2 | 117,555 978.8756 19434.76 0 5731453
x1 | 83,642 .3838383 .4863222 0 1
-------------+---------------------------------------------------------
x2 | 117,462 .0698244 .2497444 0 2
x3 | 117,555 4.151557 1.718149 0 10.63367
z1 | 117,559 .548206 .344792 0 1
z2 | 117,559 .4623664 .3309483 0 1
z3 | 117,559 .3635735 .2682159 0 1
-------------+---------------------------------------------------------
id | 117,559 6243.006 3690.662 1 12844
year | 117,559 2009.019 3.316198 2004 2015
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int(id year) byte y1 double y2 byte x1 double(x3 x2) float(z1 z2 z3 main1 main2) 1 2004 1 15 0 2.9444389791664403 .11628050303189412 .4444444 0 0 0 0 1 2005 1 25 0 3.091042453358316 .11155810978566506 .1111111 0 0 0 0 1 2006 1 30 0 3.1780538303479458 .2181349936661966 .1111111 0 0 0 0 1 2007 1 25 0 3.1780538303479458 .06591904929668757 .1111111 0 0 0 0 1 2008 1 25 0 3.2188758248682006 .08327321246305641 .1111111 0 0 0 0 1 2009 1 25 0 3.258096538021482 .09046644551097324 .1111111 0 0 0 0 1 2010 1 10 0 3.295836866004329 .20395702599256194 1 .3333333 .3333333 0 0 1 2011 1 20 0 2.995732273553991 .31468851173081386 1 .6666666 .4166667 0 0 1 2012 1 25 0 2.995732273553991 .694693505873705 1 .8333333 .4166667 0 0 1 2013 1 33 0 2.9444389791664403 1.4777636434095467 1 .8333333 .1666667 0 0 1 2014 1 70 0 2.6390573296152584 1.465341156936412 1 .6666666 0 0 0 1 2015 1 40 0 2.5649493574615367 1.2855846532976578 1 .6666666 0 0 0 2 2004 0 . . 2.3978952727983707 0 1 .6666666 .8333333 0 0 2 2005 0 . . 2.302585092994046 0 .8888889 .8333333 .8333333 0 0 2 2006 0 . . 2.302585092994046 0 .8888889 .8333333 .6666666 0 0 2 2007 1 0 0 2.302585092994046 0 .7777778 1 .5833334 0 0 2 2008 1 0 0 2.302585092994046 0 .8888889 .8333333 .5833334 0 0 2 2009 1 0 0 2.0794415416798357 0 .3333333 .6666666 0 0 0 2 2010 0 . . 2.0794415416798357 0 .6666666 .6666666 .25 0 0 2 2011 0 . . 2.0794415416798357 0 .3333333 1 .5 0 0 2 2012 0 . 1 2.0794415416798357 0 1 1 .8333333 0 373 2 2013 0 . 0 2.0794415416798357 0 .5555556 .8333333 .5 0 0 2 2014 0 . 0 2.0794415416798357 0 .5555556 .6666666 .5833334 0 0 2 2015 0 . . 2.0794415416798357 0 .5555556 .6666666 .5833334 0 0 3 2004 1 0 0 3.58351893845611 .057618833395396696 .7777778 .8333333 .6666666 0 0 3 2005 0 . 0 3.912023005428146 0 1 .6666666 .5833334 0 0 3 2006 0 . . 3.912023005428146 0 0 0 0 0 0 3 2007 0 . . 4.07753744390572 0 .6666666 .8333333 1 0 0 3 2008 0 . . 2.995732273553991 0 .6666666 .8333333 .8333333 0 0 3 2009 0 . . 3.295836866004329 0 1 .6666666 .75 0 0 3 2010 0 . . 3.6888794541139363 0 .6666666 .6666666 .6666666 0 0 3 2011 0 . . 2.772588722239781 0 .6666666 .8333333 .8333333 0 0 3 2012 0 . . 3.9512437185814275 0 1 0 .5 0 0 3 2013 0 . . 3.044522437723423 0 1 .8333333 .5 0 0 4 2004 0 . 0 .6931471805599453 .14064971291086337 .6666666 .5 .5 0 0 4 2005 1 0 1 .6931471805599453 .087 1 1 1 0 0 4 2006 1 0 0 0 0 1 1 1 0 994 4 2007 0 . 0 0 0 1 1 .75 0 0 4 2008 0 . . 0 0 .8888889 .8333333 .75 0 0 4 2009 0 . . 0 0 1 1 1 0 0 4 2010 0 . . 0 0 1 1 1 0 0 4 2011 0 . . 0 0 1 1 1 0 0 4 2012 0 . . 0 0 1 1 1 0 0 4 2013 1 30 0 .6931471805599453 .07507884950336581 1 1 1 0 0 5 2004 1 80 0 1.9459101490553132 .17195091086838932 .2222222 .5 0 0 0 5 2005 1 20 0 1.9459101490553132 .09699191448540496 .2222222 .5 0 0 0 5 2006 1 29.5 0 1.791759469228055 .17754350477437011 .2222222 .5 0 0 913.9235 5 2007 1 4.6 0 1.791759469228055 .3837559064739411 .2222222 .5 0 0 1359.132 5 2008 1 5 1 1.6094379124341003 .2130664092965216 .8888889 .5 .6666666 0 4371.1 5 2009 1 10 1 1.0986122886681098 .07197761143795689 .7777778 .5 .6666666 0 6209.525 5 2010 1 10 1 1.3862943611198906 .1939692474419783 .7777778 .5 .6666666 0 0 5 2011 1 10 1 1.6094379124341003 1.0774121445424065 .7777778 .5 .6666666 0 0 5 2012 1 80 1 1.791759469228055 .30190804450969133 .8888889 .8333333 .5 0 712.4927 5 2013 1 6 0 1.6094379124341003 0 1 .6666666 .5 0 0 5 2015 1 95 1 1.3862943611198906 0 1 .6666666 .5 0 0 6 2004 1 0 0 3.044522437723423 .023363215803786804 1 .6666666 .3333333 0 0 6 2005 1 0 0 3.091042453358316 .02488418510019133 1 .6666666 .3333333 0 36.946365 6 2006 1 0 0 3.1780538303479458 .02203491203379804 1 .6666666 .3333333 0 38.22866 6 2007 1 0 0 3.2188758248682006 .02197197542778446 1 .6666666 .3333333 0 0 6 2008 1 0 0 3.258096538021482 .024768402432117524 1 .6666666 .3333333 0 0 6 2009 1 0 0 2.8903717578961645 .043628161799596756 1 .6666666 .3333333 0 0 6 2010 1 0 0 2.8903717578961645 .047617787730093966 1 .6666666 .3333333 0 0 6 2011 0 . 0 2.3978952727983707 0 1 .6666666 .3333333 0 0 6 2012 0 . . 2.0794415416798357 0 1 0 .3333333 0 0 7 2004 0 . . 5.783825182329737 0 0 0 0 0 0 7 2005 0 . . 5.730099782973574 0 .1111111 0 0 0 0 7 2006 1 0 1 5.181783550292085 0 0 0 0 0 0 7 2007 1 0 0 4.844187086458591 0 .4444444 .5 .5 0 0 7 2008 1 0 0 4.762173934797756 0 .8888889 .8333333 .75 0 0 7 2009 0 . . 4.762173934797756 0 .8888889 .8333333 .75 0 0 7 2010 0 . . 4.727387818712341 0 .5555556 .3333333 .3333333 0 0 7 2011 0 . . 4.727387818712341 0 .8888889 0 0 0 0 7 2012 0 . . 4.700480365792417 0 0 0 0 0 0 7 2013 0 . . 4.727387818712341 0 .2222222 0 0 0 0 7 2014 0 . . 4.770684624465665 0 .7777778 1 .5 0 0 7 2015 0 . . 4.7535901911063645 0 0 .1666667 0 0 0 8 2004 1 77 1 1.9459101490553132 .23924395665200565 1 .3333333 .3333333 0 0 9 2004 1 0 0 2.772588722239781 0 .1111111 0 0 0 1527.875 9 2005 0 . 0 2.4849066497880004 .08102188765002487 .3333333 .3333333 .3333333 0 406.1347 9 2006 0 . 0 2.4849066497880004 0 .3333333 0 .25 0 0 9 2007 0 . 0 2.5649493574615367 0 .2222222 0 0 0 0 9 2008 1 1 0 2.5649493574615367 .0037338232374995446 .3333333 0 0 0 0 11 2004 1 0 1 3.2188758248682006 .020868634055714878 .5555556 .5 .3333333 4717.745 0 11 2005 1 0 1 3.295836866004329 .022795597436482 .5555556 .5 .3333333 4528.9175 0 11 2006 1 20 1 3.4965075614664802 .012718997364693722 .7777778 .3333333 .4166667 3297.949 0 11 2007 1 0 1 3.5553480614894135 .008183939809835146 .5555556 .3333333 .25 2851.6885 58.19772 11 2008 1 20 1 3.4011973816621555 .01709749583382931 .5555556 .3333333 .25 0 3072.7476 11 2009 1 20 1 3.4011973816621555 .01871897821983031 .5555556 .3333333 .25 0 3206.669 20 2004 1 .5 1 4.204692619390966 .017469072190368716 .6666666 .5 .4166667 1461.2487 186.1568 20 2005 0 . 1 4.174387269895637 .046790002310655526 .6666666 .6666666 .5833334 1339.4368 0 20 2006 1 0 1 4.1588830833596715 .03908659000351231 .6666666 .8333333 .6666666 1520.8528 0 20 2007 1 4.6 1 4.143134726391533 .04142966294820746 .6666666 .8333333 .6666666 2200.8137 0 20 2008 1 4.6 1 4.143134726391533 .0453161594348968 .6666666 .8333333 .6666666 2288.92 0 20 2010 1 20 1 4.330733340286331 .02959460778214954 .6666666 0 .5 8027.119 4961.746 20 2011 1 20 1 5.0238805208462765 .10545263729220362 .6666666 0 .5 0 0 20 2012 1 20 1 4.543294782270004 .06503342386711894 .5555556 .6666666 .6666666 6628.726 17475.73 20 2013 1 20 1 4.61512051684126 .0751187815657481 .6666666 .6666666 .6666666 21016.836 34786.49 20 2014 0 . 1 4.61512051684126 .07284496273083095 .6666666 .6666666 .6666666 22037.18 36475.33 21 2004 0 . . 7.774015077250727 0 .1111111 0 0 0 0 21 2005 0 . . 7.419979923661835 0 0 0 0 0 0 end
↧
↧
Survey data
Good morning
I need some advice for my master thesis please.
My data is a cross-sectional survey. About 11000 households. I'm studying the effect of some disease on the investment in human capital of the siblings of the children who are sick.
I have the disease status of children under 5 years old in each household. I've created a dummy variable which takes the values 0, 1 or 2 if there is 0,1 or 2 sick children in the household. My equation looks like this:
EDUij = ⍺i + 𝜸j + 𝜷1 STATUSj + 𝜷2 EDU_MOTHERij + 𝜷3 WEALTH_INDEXj +... + 𝛆ij
i for a child, j for a household.
I am wondering how to specify to Stata that my right-hand-side variable is valid only for children who are NOT sick, so that i can see if they have been impacted in terms of education due to the sickness of their siblings.
I hope it's clear enough.
Thank you for your help
I need some advice for my master thesis please.
My data is a cross-sectional survey. About 11000 households. I'm studying the effect of some disease on the investment in human capital of the siblings of the children who are sick.
I have the disease status of children under 5 years old in each household. I've created a dummy variable which takes the values 0, 1 or 2 if there is 0,1 or 2 sick children in the household. My equation looks like this:
EDUij = ⍺i + 𝜸j + 𝜷1 STATUSj + 𝜷2 EDU_MOTHERij + 𝜷3 WEALTH_INDEXj +... + 𝛆ij
i for a child, j for a household.
I am wondering how to specify to Stata that my right-hand-side variable is valid only for children who are NOT sick, so that i can see if they have been impacted in terms of education due to the sickness of their siblings.
I hope it's clear enough.
Thank you for your help
↧
Roc curve after beta regression
Hi everyone, I want to perform Roc analysis after beta regression and fractional regression. I have estimated predict value but it tells me that pr must be 0 or 1 for applying Roc curve. Any suggestion?
↧
tebalance summarize coefplot
Hi everyone,
I am working (still in process on the matching model) on a project using teffects psmatch with a number of outcome and treatment variables. The number variables has prompted me to get creative with how I display my diagnostics. Following a teffects psmatch estimation and in checking for the balance of the match, I use the
command. One example result (sorry for the X var placement) is
What I would like to do is use the coefplot command to take the standardized differences (raw and match) and variance ratios (raw and matched) and plot them with an xline(0) line for standardized differences and an xline(1) for the variance ratios. Then, for each tebalance summarize table, combine into a single, two pane graphic. I have been playing with coefplot command, but cannot get it to work. Any advice on doing so would be greatly appreciated.
Sincerely,
Eric
I am working (still in process on the matching model) on a project using teffects psmatch with a number of outcome and treatment variables. The number variables has prompted me to get creative with how I display my diagnostics. Following a teffects psmatch estimation and in checking for the balance of the match, I use the
Code:
tebalance summarize
PHP Code:
.
Treatment-effects estimation Number of obs = 5,357
Estimator : propensity-score matching Matches: requested = 1
Outcome model : matching min = 1
Treatment model: logit max = 2
---------------------------------------------------------------------------------------
| AI Robust
Yvar | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
ATE |
Treatment |
(yes vs No) | .1712712 .0297238 5.76 0.000 .1130136 .2295289
---------------------------------------------------------------------------------------
. tebalance summarize
note: refitting the model using the generate() option
Covariate balance summary
Raw Matched
-----------------------------------------
Number of obs = 5,357 10,714
Treated obs = 3,915 5,357
Control obs = 1,442 5,357
-----------------------------------------
-----------------------------------------------------------------
|Standardized differences Variance ratio
| Raw Matched Raw Matched
----------------+------------------------------------------------
|
X1 | .0239846 -.0216856 1.003129 .9976267
|
x2| -.0883955 -.0084951 1.072024 1.040323
x3| .03316 .0037765 .9799571 .9515908
x4 | .0745632 -.0019823 1.018004 1.045509
|
X5 | .2021603 .0005313 .6816709 .9989242
|
x6 | -.1715681 -.0101372 1.098919 .9468103
x7 | .0766124 .0212042 .9688859 .9993285
x8 | .0059114 .0452078 1.0091 .9892231
x9 | .0704363 .0558643 1.03551 .9940278
x10 | .1198441 .0224827 .9024419 .8567823
|
|
x11 | -.1158196 .0448593 .7736173 1.11359
x12 | .1435748 .0308307 1.398567 1.069149
x13 | .0225676 -.0165485 1.057586 .9601829
x14 | -.1680311 .0230644 .6564869 1.063654
x15 | -.1101453 -.0098 .7807659 .9771978
x16 | .0683352 -.0480281 1.1599 .9064829
x17 | .1115147 -.0112496 1.277536 .9771073
-----------------------------------------------------------------
Sincerely,
Eric
↧
Conditions for a logistic regression?
Hey everyone,
I have a question considering the conditions on how you should prepare your data for logistical regression in Stata.
We have an assignment, where we have to use the logit-command for a binary logistical regression.
In a regular regression, you would need to control for multi-correlation , white-test, etc.
What would one need to check for a logit-command?
I have a question considering the conditions on how you should prepare your data for logistical regression in Stata.
We have an assignment, where we have to use the logit-command for a binary logistical regression.
In a regular regression, you would need to control for multi-correlation , white-test, etc.
What would one need to check for a logit-command?
↧
↧
programming a dummy
hello,
I am struggling a lot with how to program my dummy. I want to generate a dummy that equals 1 if the firm was an SME at the end of 2012, and 0 if the firm was not. In order to be an SME, a firm has to be between certain boundaries for a couple of years before. Therefore, I already programmed whether or not the firm was within these boundaries from 2010 to 2012, I named these SME2010 SME2011 SME2012. The firm will have the status 'SME' at the end of 2012 if the firm has exceeded these limits for no more than one of these three years. I, however, have no idea how to program this correctly. Could you please help me?
thanks a lot!
Timea De Wispelaere
I am struggling a lot with how to program my dummy. I want to generate a dummy that equals 1 if the firm was an SME at the end of 2012, and 0 if the firm was not. In order to be an SME, a firm has to be between certain boundaries for a couple of years before. Therefore, I already programmed whether or not the firm was within these boundaries from 2010 to 2012, I named these SME2010 SME2011 SME2012. The firm will have the status 'SME' at the end of 2012 if the firm has exceeded these limits for no more than one of these three years. I, however, have no idea how to program this correctly. Could you please help me?
thanks a lot!
Timea De Wispelaere
↧
xtlogit, margins dydx with interaction terms
I am working with a logit panel data model with interaction terms.
My objective is to estimate the marginal effect of one of the regressors (call it X1) on my dep. variable Y. My regressor of interest also appears interacted with other regressors (say X2). My model has both groups and time fixed effects (for simplicity I will drop the time fixed effects from my explanation below since I believe they are not a concern for my main argument).
I can easily compute margins, dydx under the assumption that the groups fixed effects are 0. However, I would like to keep the fixed effects in the margin calculation. I am following the Mundlak-Chamberlain method (see https://www.stata.com/meeting/spain1...s14_pinzon.pdf ). However this "transformed" model contains interaction terms which prevent the use of margins in the usual manner (see on this the last page of https://journals.sagepub.com/doi/10....867X1301300105).
The turnaround that I follow is instead to write down and plug-in my model as an expression() within the margin command; specifically after estimating the model I manually write down
dp/dX1 = e^(-g(Y,X1,X2))/(1+e^(-g(Y,X1,X2))) * g'(Y,X1,X2)
and the command becomes:
margins, expression(dp/dX1) , over(year ID)
where ID is the group variable and year is the time.
I want to compute the marginal effect of a change in X1 for each possible level of X2 and this is why I include the over(year ID).--on a side note, is this equivalent to specifying at() in my case?
Is my approach correct for what concerns estimates and (delta est) standard error? If not, are there other solutions?
My objective is to estimate the marginal effect of one of the regressors (call it X1) on my dep. variable Y. My regressor of interest also appears interacted with other regressors (say X2). My model has both groups and time fixed effects (for simplicity I will drop the time fixed effects from my explanation below since I believe they are not a concern for my main argument).
I can easily compute margins, dydx under the assumption that the groups fixed effects are 0. However, I would like to keep the fixed effects in the margin calculation. I am following the Mundlak-Chamberlain method (see https://www.stata.com/meeting/spain1...s14_pinzon.pdf ). However this "transformed" model contains interaction terms which prevent the use of margins in the usual manner (see on this the last page of https://journals.sagepub.com/doi/10....867X1301300105).
The turnaround that I follow is instead to write down and plug-in my model as an expression() within the margin command; specifically after estimating the model I manually write down
dp/dX1 = e^(-g(Y,X1,X2))/(1+e^(-g(Y,X1,X2))) * g'(Y,X1,X2)
and the command becomes:
margins, expression(dp/dX1) , over(year ID)
where ID is the group variable and year is the time.
I want to compute the marginal effect of a change in X1 for each possible level of X2 and this is why I include the over(year ID).--on a side note, is this equivalent to specifying at() in my case?
Is my approach correct for what concerns estimates and (delta est) standard error? If not, are there other solutions?
↧
Recoding to a categorical variable_'the number of new and transformed varnames should be the same r(198)'
Hi there,
I am trying to recode age in my dataset (min 40, max 70) into 7 categories;
recode age 40/44 = 40-44 45/49 = 45-49 50/54 = 50-54 55/59 = 55-59 60/64 = 60
> -64 65/69 = 65-69 70/74 = 70-74, generate(Age groups)
OR
recode age 40/44 = A 45/49 = B 50/54 = C 55/59 = D 60/64 = E 65/69 = F 70/74
> = G, generate(Age groups)
but then getting this error message;
the number of new and transformed varnames should be the same
r(198);
Please assist?
Thank you.
I am trying to recode age in my dataset (min 40, max 70) into 7 categories;
recode age 40/44 = 40-44 45/49 = 45-49 50/54 = 50-54 55/59 = 55-59 60/64 = 60
> -64 65/69 = 65-69 70/74 = 70-74, generate(Age groups)
OR
recode age 40/44 = A 45/49 = B 50/54 = C 55/59 = D 60/64 = E 65/69 = F 70/74
> = G, generate(Age groups)
but then getting this error message;
the number of new and transformed varnames should be the same
r(198);
Please assist?
Thank you.
↧