I hope anyone can help me with my regressions with loops because I am quite new to use multiple loops. I have:
- 2 different dependent variables: a and b
- 5 different independent variables: c, d, f, g and h
- One same set of control variables
- And I have another variable z, which is divided into terciles
I would like to run each following regression based on the terciles of z:
reg a c controls
reg a d controls
reg a f controls
reg a g controls
reg a h controls
reg b c controls
reg b d controls
reg b f controls
reg b g controls
reg b h controls
Also, I want to use outreg2 to extract the results in order (for example, reg a c controls (1st tercile), reg a c controls (2nd tercile) and reg a c controls (3rd tercile). Then reg a d controls (1st tercile), reg a d controls (2nd tercile) and reg a d controls (3rd tercile) ...........................reg b h controls (1st tercile), reg b h controls (2nd tercile) and reg b h controls (3rd tercile).
I have tried to write the code like this, but it gives me a table with over 100 columns. Can anyone help me, please?
I really appreciate your help.
Code:
xtset id year xtile z_tercile = z,nquantiles(3) global controls = "x1 x2 x3 x4 x5" local indvar1 F.a local indvar2 F.b local dvar1 c local dvar2 d local dvar3 f local dvar4 g local dvar5 h forv i=1/3{ forv d=1/5{ forv j=1/2{ xtreg `indvar`j'' `dvar`d'' $controls if z_tercile==`i', fe outreg2 using table.xls, append stats(coef se) dec(4) sortvar(c d f g h $controls) } } }
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long id double year float(a b c d f g h x1 x2 x3 x4) double x5 float z 1 2003 .05740558 .6179267 .0002737476 .07694252 0 1.7885895e-18 -.7820968 4.625806 0 2.6390574 .7210243 0 .6931472 1 2004 .0649916 .6347956 .00025634453 .07694128 0 1.7885895e-18 -.7893308 4.590917 .0101875 2.708233 .7519287 0 1.0986123 1 2005 .05914634 .43740895 .00023952096 .08335163 0 1.7885895e-18 -.796324 4.6095705 .014796753 2.77276 .8061169 0 1.3862944 1 2006 .04168578 .3297939 .0002274278 .07693923 0 1.7885895e-18 -.8013507 4.6997027 .015178077 2.8333745 .8484832 .2 1.0986123 1 2007 .017250383 .26725262 .0006476684 .1538206 0 1.3404966e-11 -.6266699 4.7348423 .018105585 2.890524 .8426654 .2667 1.3862944 1 2008 .016824573 .26758727 .000617538 .1573849 0 1.6064574e-16 -.6391943 4.778876 .01830997 2.9447274 .8562811 .32 .6931472 1 2009 .010607123 .18055393 .0006076565 .1680738 0 1.0341008e-16 -.643302 4.846248 .019844953 2.996006 .9456088 .34 1.609438 1 2010 .012180088 .2050075 .0008130081 .1687243 .000598591 6.644726e-18 -.5579432 4.835881 .022461114 3.044783 .8313994 .54 .6931472 1 2011 .018037405 .20919257 .0008130081 .1687243 .000598591 6.644726e-18 -.5579432 4.905014 .02681849 3.0912914 .8649291 .24 .6931472 1 2012 .015667645 .16122043 .0002183883 .07693751 0 1.7885895e-18 -.8051081 4.947325 .018605324 3.1358516 .8229198 .36 .6931472 1 2013 .006773706 .10351888 .0002254283 .07693751 0 1.7885895e-18 -.8021818 5.011819 .0241362 3.178396 .7890258 .25 1.0986123 1 2014 .004964413 .10086903 .0002254283 .07693751 0 1.7885895e-18 -.8021818 5.05162 .027025057 3.2192044 .8287393 .1767 .6931472 1 2015 .004425053 .08169895 .00022727273 .0833507 0 1.7885895e-18 -.8014152 5.024065 .032209024 3.2584126 .8099487 .22 1.0986123 1 2016 .0032249265 .0959488 .00022815423 .07693928 0 1.7885895e-18 -.8010487 5.080141 .04677815 3.296243 .8995772 .24 1.0986123 1 2017 .0023096246 .1461774 .0002428953 .07694033 0 1.7885895e-18 -.7949212 5.175416 .04380349 3.332596 .9172587 .26 1.3862944 2 2003 .02612241 .3924782 .0010949904 .1778481 .008772796 .0039610784 -.4407313 6.564267 0 3.4875846 .5732008 0 1.0986123 2 2004 .01289697 .3268641 .0025634454 .1966329 .17614095 .012056245 .1696654 6.530892 0 3.5177824 .5656238 0 .6931472 2 2005 .0045446623 .20836908 .0028742515 .20228693 .2470284 2.8241735e-07 .2988595 6.763181 0 3.547016 .677748 0 .6931472 2 2006 .001798926 .11524795 .002501706 .19851913 .13199544 7.290945e-09 .14400117 6.804937 0 3.575419 .6036456 0 1.609438 2 2007 .0013387145 .15850027 .001943005 .20214716 .04211038 5.136419e-09 -.088236 7.030557 0 3.603038 .6151881 0 2.0794415 2 2008 .0038360446 .2009262 .001440922 .18784317 .015204014 1.5672164e-16 -.29693758 7.059974 0 3.629987 .5556599 0 2.3025851 2 2009 .0037338636 .0908347 .001215313 .19067666 .013338535 3.0150275e-16 -.39071685 7.108955 0 3.656158 .5589364 0 2.0794415 2 2010 .002989111 .066077255 .0014227643 .20549662 .05288613 1.9892816e-16 -.3044849 7.199551 0 3.6816616 .5592807 .075 2.0794415 2 2011 .003093391 .05334071 .0014227643 .20549662 .05288613 1.9892816e-16 -.3044849 7.411969 0 3.706531 .5291632 .3 2.0794415 2 2012 .003450312 .08073868 .001528718 .20599218 .0396458 3.823008e-10 -.26044324 7.349195 0 3.7308624 .4789042 .3 1.609438 2 2013 .002576775 .05417194 .0018034265 .19309625 .03811708 1.553727e-10 -.1462547 7.335104 0 3.754552 .505724 .3 1.94591 2 2014 .0023178835 .04430104 .0018034265 .19309625 .03811708 1.553727e-10 -.1462547 6.92325 0 3.777693 .48509565 .3 1.7917595 2 2015 .00206578 .0457977 .0013636363 .2025596 .010970575 8.650853e-09 -.3290634 6.847508 0 3.8003106 .61159 .3 1.609438 2 2016 .002769194 .04323253 .0009126169 .19533826 .01032933 4.674557e-08 -.516539 6.848845 0 3.822488 .6338278 .3 1.609438 2 2017 .0017740118 .032168075 .0009715813 .1715631 .02921264 2.270759e-16 -.4920285 6.811977 0 3.8441255 .50186956 .3 1.3862944 3 2005 .0005424603 .07031573 .006467066 .2123277 .2797776 1.4849527e-07 1.7922888 7.21312 .04664495 3.3255286 .4456343 0 2.944439 3 2006 .0004448615 .06746279 .005458267 .2112622 .28150406 3.93027e-07 1.3729603 7.216596 .04492987 3.360851 .4317414 0 3.0910425 3 2007 .00050151534 .09666108 .005829016 .216075 .27930954 2.6268037e-08 1.5270698 7.289632 .0394379 3.394968 .646658 0 2.995732 3 2008 .0015107996 .14773382 .005351997 .22141197 .21180776 5.8991e-17 1.3287855 7.392541 .04346694 3.4280484 .6540202 0 2.890372 3 2009 .0015849762 .16180916 .003038282 .218432 .10105354 3.08965e-17 .3670397 6.998148 .04860077 3.459983 .6479015 0 2.397895 3 2010 .00009596899 .08681683 .004878049 .21952525 .15408178 2.6053685e-17 1.1317782 7.055051 .04727026 3.4909296 .7483473 0 2.70805 4 2005 .0047207 .1713632 .0009580838 .19113675 .0040155784 1.0106709e-07 -.4976397 6.683342 0 3.6350596 .4340151 .415 1.3862944 4 2006 .003229072 .12769769 .0009097112 .19190817 .0010546603 3.887981e-08 -.5177469 6.755636 0 3.6611 .5074153 .435 1.3862944 4 2007 .0022369374 .15347514 .00021588946 .18585242 0 6.493791e-09 -.8061484 6.835525 0 3.686479 .4516935 .47 1.0986123 4 2008 .004859281 .1954047 .0016467682 .2070406 .05479844 1.952668e-18 -.211373 7.177894 0 3.7112975 .3155712 .495 1.0986123 4 2009 .004786891 .1041494 .0016204172 .2139545 .0225729 4.583826e-17 -.22232695 7.122264 0 3.735449 .28163427 .515 .6931472 4 2010 .0027998574 .05379133 .0010162601 .21389444 .015079642 6.77241e-17 -.4734577 7.104129 0 3.7590315 .29027265 .535 1.3862944 4 2011 .002873957 .0543122 .0010162601 .21389444 .015079642 6.77241e-17 -.4734577 7.256547 0 3.7820704 .29499155 .555 1.609438 4 2012 .0018365952 .0577327 .0006551649 .1818868 .015613783 1.4554608e-10 -.6235541 7.215377 0 3.804651 .28515923 .575 1.7917595 4 2013 .0019852163 .04267524 .0011271415 .1779668 .017036226 1.3531802e-10 -.4273669 7.297929 0 3.826674 .3124125 .595 1.94591 4 2014 .0016854556 .04595727 .0011271415 .1779668 .017036226 1.3531802e-10 -.4273669 7.293059 0 3.848222 .3202278 .615 2.0794415 4 2015 .0011345441 .04072658 .0020454545 .21651413 .1331242 1.0710759e-07 -.0456495 7.246783 0 3.8693156 .3250595 .635 2.0794415 4 2016 .0008595755 .033033803 .0015970797 .1959671 .10749505 1.2658522e-07 -.23202603 7.265352 0 3.8900294 .3412485 .655 1.7917595 4 2017 .0006994272 .02909028 .000728686 .16388026 .06197111 1.408617e-16 -.5929924 7.728491 0 3.910268 .3132935 .675 1.94591 5 2013 .00010507122 .024198003 .003155996 .2015814 .11627 3.403482e-09 .4159705 8.84072 0 1.0556953 .29048213 .5 2.1972246 end label values id firm label def firm 1 "000360206", modify label def firm 2 "000361105", modify label def firm 3 "000886309", modify label def firm 4 "000957100", modify label def firm 5 "00101J106", modify
0 Response to Multiple loops of varlists and outreg2 for quantile regressions
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