Hi,

I would like to calculate the moving average of _b_LogSize _b_LogBM _b_MOM12 _b_cons by months of the year over the last 10 years. For instance, I would like to average:
[_b_LogSize(Jan2020)+_b_LogSize(Jan2019)+... + _b_LogSize(Jan2010)]/10
I would like to do it for each month of the year and the 4 variables above.

Currently, I am using "tsegen" but it is only performing a basic 10 years rolling average without taking into account months. Please can you help? Thanks!

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(date _b_LogSize _b_LogBM _b_MOM12 _b_cons month)
493    .09046634   4.1176205  2.5190666 -1.2977983  2
494   -2.0309033   .22475155  -9.759151  30.883093  3
495   -1.0832306     .406494  1.2204374   12.85686  4
496    -2.059664  -.24260585 -2.1624966  17.363857  5
497     .0960215   1.4607705   .5428929  1.1389401  6
498   -.51043904   1.9077493   2.975317  3.2279406  7
499    1.4333166   1.5936514   8.690335 -22.944815  8
500    -3.742916  -2.0467317 -10.620685   37.35859  9
501   -1.5358274  -.21344493  -11.50268   21.56925 10
502   -1.3188688   1.5866338  -1.571547  17.318272 11
503     .4737448   1.0378938   5.982695  -3.458642 12
504     .6831618   2.8478656  2.8225315 -3.0741665  1
505    -1.461657  -.15056618  -7.833986   19.39885  2
506    .15322176    3.042314   8.148885 -1.4166797  3
507    1.3517747   1.4202607   3.542518  -12.55804  4
508     .5102485    2.402631   9.466259   -8.92338  5
509    .14730057  -2.0097933  3.9253964 -12.569436  6
510   -.13011447   2.1992843 -1.5208116  4.3657007  7
511    -.7876962  -1.2948427  12.139635  -1.489981  8
512    .11407951   -.9536515 -10.651284   3.702368  9
513   -3.1914134    3.209994  -19.96686    39.4386 10
514      .485197   2.1473858  10.600106  -6.581267 11
515    -.3188047    .3255054 -1.9797972   .2980764 12
516     .1177141   -.3202377  1.3013583 -2.6397576  1
517   -1.1778133   1.3565993  -.6570513  12.859035  2
518   -.06110432    .7163003  -13.77926   7.564542  3
519    -3.706227    .1257015   -21.8051   38.16873  4
520   .013643764    .7770253  -.7633198  1.8524495  5
521    -2.111613   -.4619206 -2.8396015  22.133587  6
522    -.6336941     .363702   1.210958   9.975827  7
523    -1.014817    .5368967  -2.220904   9.022312  8
524    -.5406848   -.6548046   4.404526  10.582654  9
525    -.7146962   .26931196 -1.1149786   9.302611 10
526     .6385671   2.2701182 -1.6060712  1.2781098 11
527    -.7188457   .57633734  3.1786885    8.57283 12
528    -.4600639    .9384994  -2.588896   8.649567  1
529    -.8613429    .9671511   1.099575   8.608756  2
530   -.07247534   -.1537994 -4.3157163   .8893291  3
531   -.56535167   .07716002   3.332622   5.861691  4
532    -.4648149    1.094499  2.3134766   7.434675  5
533    .27500623   2.5805945 -2.3379977   -2.46115  6
534   -.12708639   1.0634447  -2.841303   3.477974  7
535    -.8708178   .23912176  3.3763766  10.540322  8
536   -.54018456    .7189706 .036027018   8.435553  9
537   -1.4168496   1.6745526   3.554173  19.626165 10
538    -.8309463  -1.2100756  -3.173917  11.207634 11
539     .4382522   1.5968238  3.6161275  -5.716834 12
540    .09637992   2.2883317   2.479093    3.72967  1
541    -.2498437    1.620358   .7389261   2.624726  2
542    -.4497205  -.22138956  -4.454505  2.4982936  3
543   -1.2707784    .2922744  2.3344254  16.399544  4
544   -1.0866243   1.5483017    4.93187  12.820357  5
545    -.6698218   -.3213176  1.0515611  11.332736  6
546   -.04462296   1.1982578  4.1905856  .09844476  7
547 -.0022533806   .58677596  4.4705863   .9089295  8
548   -.26722765   -2.316635 -1.8966275  -.8938839  9
549    -.7312025   -1.262661  2.9972234   9.407964 10
550   -.25243318    .7990872   2.407081    3.20534 11
551   -1.7431985   -.8312908   8.716989  18.789204 12
552    -.6822688  -.11647125  -5.275173   7.973406  1
553    -1.329878   -.7738516   2.598626  13.546588  2
554     .6247546   1.2956816   .6266102  -3.531033  3
555  -.072735175 -.015340515  -3.187542  -1.636328  4
556    .23074944   -.3995629  2.8622246  -2.874943  5
557     .8129722    3.236957  -.8909066 -4.6846304  6
558    -.6832631  -1.0202931  -7.254835   9.369593  7
559   -.58940864  -1.5918627 -4.2435346    7.06904  8
560   -1.2256618   -.6868519  -.5801456  15.051693  9
561    -.8219826    .3530865   -4.62138   11.96707 10
562     .6590861    1.541397  2.6057794  -4.902538 11
563    -.8354015   -.8621215   2.812942   9.531097 12
564    -.9848793   1.3190557 -1.2905746   10.60272  1
565     .1217109   .48999825  1.0703242  .04173471  2
566     .1686769   -.4838383 -1.5908992  1.8760736  3
567   -.05383684    .5829975 -.28491843   4.785616  4
568    .03835373  -.19093046 -.13494432 -2.3227618  5
569    .23461634  -2.2530422  -.7020459  -7.838483  6
570    -.4224021   -.7332852     .83166   4.854007  7
571     .7889878   -.4763553   6.132439  -5.976163  8
572    -.4769273   .21490236   5.495591   6.372781  9
573     .6893386    .7729292   2.860333 -10.344966 10
574   -.12302468    .3361884   7.454767  -.4429162 11
575   -.11429738    3.273526 -10.505454    .959588 12
576    -.8703886    .2214929    7.45836   5.192826  1
577    .08055088   -.8899767 -1.0208174 -1.7982605  2
578   -1.0253339    .5415361   4.950213   16.10081  3
579    -1.318757   -.4902536   4.123805  15.095932  4
580   -.04682831  -1.1088916  11.664658 -10.094048  5
581   -1.4003263   1.8397713   -5.47766    16.4739  6
582    -.8646321   .17430896  -8.737585  10.405688  7
583    -.5431101   2.2606583 -1.1048132  -.7501085  8
584     .9161993    .5219336   2.973937  -27.47846  9
585       .62261   -.6762373   4.851254  -14.31035 10
586   -1.4068698   1.9171596  -9.508333  15.039592 11
587    -.7277468  -2.5405514    5.02278 -1.5999113 12
588    -.4634641  -3.9733584  -.3435096  -8.650168  1
589     .1495674  -.18204223  -20.42709  1.6548795  2
590      -4.0047   1.1798627  -55.92088  30.824076  3
591     1.038709    3.684857 -13.146856  -6.878941  4
592     -.822459    .1430246   9.767742  11.020597  5
end
format %tm date
Code:
local t 1
local model1 "LogSize LogBM MOM12"
    *** 10 year rolling window ***
        tsegen r_cons = rowmean(L(1/120).(_b_cons))
        drop _b_cons
        foreach var in `model`t''{
            tsegen r_`var' = rowmean(L(1/120).(_b_`var'))
            drop _b_`var'
        }