Obtaining Beta coefficients for Monthly returns data

[David Connaughton]

Hi, this is my first time on this forum so forgive me if I post in the wrong manner.

I am attempting to obtain beta coefficients for the variables MktRF SMB and HML but am having trouble doing so and can't find a solution on any forum I have came across, or at least I am not understanding the solution people have suggested on other forums.


input long(Dom Date) float(w_rollingperformance inflationrate marketcap gdppercapita) double(MktRF SMB HML RMW CMA RF) float humandevelopmentindexundp
1 320 0 2.7326 125.276 1.84577 2.06 2.2 .77 .95 -.09 .1 .904
1 321 -1.834537 2.7326 125.276 1.84577 2.2 2.29 1.43 -3.64 1.44 .07 .904
1 322 1.5608537 2.7326 125.276 1.84577 2.73 2.89 1.28 -1.48 1.66 .07 .904
1 323 4.1593857 2.7326 125.276 1.84577 1.32 2.94 .1 1.61 -.31 .08 .904
1 324 6.626717 2.7326 125.276 1.84577 . . . . . . .904
1 325 1.3333676 2.7326 125.276 1.84577 . . . . . . .904
1 326 5.460494 2.7326 125.276 1.84577 . . . . . . .904
1 327 3.224067 2.34326 126.416 3.00431 2.29 2.81 1.85 -1.83 2.31 .07 .907
1 331 .7118458 2.34326 126.416 3.00431 1.92 .22 .69 1.17 -1.18 .06 .907
1 332 -1.0775551 2.34326 126.416 3.00431 -.03 3.93 .45 1.18 -1.34 .09 .907
1 333 -3.037711 2.34326 126.416 3.00431 -2.34 -.64 -1.67 2.91 -.76 .08 .907
1 334 .6079404 2.34326 126.416 3.00431 .67 -1.55 .23 -.69 -.46 .06 .907
1 335 .52197164 2.34326 126.416 3.00431 2.38 2.48 1.02 1.02 .1 .08 .907
1 336 3.921357 2.34326 126.416 3.00431 -3.79 -1.21 2.33 2.07 -.37 .1 .907
1 337 3.0772076 2.34326 126.416 3.00431 .36 -.5 1.25 .46 -.5 .11 .907
1 338 7.728163 2.34326 126.416 3.00431 2.15 1.22 .48 -.53 -.13 .11 .907
1 339 9.806663 2.34326 126.416 3.00431 . . . . . . .907
1 340 11.51192 2.34326 126.416 3.00431 . . . . . . .907
1 341 9.632057 2.34326 126.416 3.00431 . . . . . . .907
1 342 8.048813 2.69183 115.673 1.80778 -1.93 2.41 1.48 2.1 -.23 .16 .908
1 343 -7.195466 2.69183 115.673 1.80778 -2.68 -.14 .23 -1.29 .37 .27 .908
1 344 .19569016 2.69183 115.673 1.80778 2.9 .07 -.74 -.59 -1.08 .31 .908
1 345 9.287386 2.69183 115.673 1.80778 2.58 2.14 -.53 .75 -.69 .32 .908
1 346 8.1737795 2.69183 115.673 1.80778 2.99 .62 1.41 1.12 .49 .16 .908
1 347 -2.262076 2.69183 115.673 1.80778 -2.02 -.26 1.55 .41 .57 .21 .908
1 348 -2.357272 2.69183 115.673 1.80778 -2.6 -.93 .05 .38 -.32 .21 .908
1 349 .04123825 2.69183 115.673 1.80778 1.43 -.94 -.08 -.69 .28 .24 .908
1 350 7.47205 2.69183 115.673 1.80778 1.23 2.08 1.63 .84 .05 .23 .908
1 351 4.868564 2.69183 115.673 1.80778 3.54 1.02 -.15 -.2 -.45 .24 .908
1 352 3.725349 2.69183 115.673 1.80778 1.04 .64 .8 .21 .38 .3 .908
1 353 8.69897 2.69183 115.673 1.80778 2.08 -.19 .96 .57 -.79 .29 .908
1 354 2.376752 3.55529 146.592 1.22256 4.52 2.78 1.41 -.76 -1.14 .35 .911
1 355 6.69387 3.55529 146.592 1.22256 3.33 .46 -.37 .49 -.84 .41 .911
1 356 6.110798 3.55529 146.592 1.22256 2.29 .3 .4 -.04 -.26 .42 .911
1 357 7.35193 3.55529 146.592 1.22256 1.85 -.12 1.36 -.06 .37 .4 .911
1 358 2.6591456 3.55529 146.592 1.22256 -.46 -.63 1.35 -1.12 .56 .34 .911
1 359 4.81953 3.55529 146.592 1.22256 2.43 1.98 .43 -.54 -.35 .37 .911
1 360 10.360887 3.55529 146.592 1.22256 2.87 .55 1.32 1.37 -.37 .36 .911
1 361 -4.7395844 3.55529 146.592 1.22256 -4.12 -2.04 1.17 .91 1.28 .43 .911
1 362 7.434034 3.55529 146.592 1.22256 -.59 -1.63 .1 .6 -.28 .4 .911
1 363 -3.344086 3.55529 146.592 1.22256 -.25 -2.78 2.5 .32 .8 .4 .911
1 364 2.868844 3.55529 146.592 1.22256 2.23 .13 -.74 -.58 .61 .42 .911
1 365 1.592429 3.55529 146.592 1.22256 .6 -1.33 .5 -.04 1.14 .41 .911
1 366 .4247295 2.32761 152.035 3.12229 .93 .93 .13 -.37 .63 .44 .915
1 367 6.83997 2.32761 152.035 3.12229 3.18 .66 -3.01 1.16 -1.44 .32 .915
1 368 -1.810094 2.32761 152.035 3.12229 -4.65 -3.39 -.65 .83 2 .34 .915
1 369 1.0635753 2.32761 152.035 3.12229 -1.53 -.83 .76 .13 -.07 .27 .915
1 370 .4305763 2.32761 152.035 3.12229 -.55 1.51 .78 .14 .05 .38 .915
1 371 7.267241 2.32761 152.035 3.12229 1.76 .22 .48 .46 -.2 .43 .915
1 372 9.770726 2.32761 152.035 3.12229 3.71 -.81 .31 .37 .24 .44 .915
1 373 3.909745 2.32761 152.035 3.12229 2.49 -.77 .63 .5 -.21 .41 .915
1 374 7.252221 2.32761 152.035 3.12229 -.79 1.08 .06 1.11 -.32 .4 .915
1 375 -2.5075 2.32761 152.035 3.12229 -2.37 .23 -.9 .69 -.85 .4 .915
1 376 -5.857945 2.32761 152.035 3.12229 -1.14 -3.48 -.48 -.17 .86 .42 .915
1 377 12.267598 2.32761 152.035 3.12229 4.42 -2.34 -1.63 -.22 -2.33 .32 .915
1 378 -3.742432 4.3503 64.8141 1.52197 -7.85 -.81 2.68 -1.17 2.45 .21 .92
1 379 -13.290196 4.3503 64.8141 1.52197 -19.51 -2.56 .03 1.85 5.46 .08 .92
1 380 -1.6142213 4.3503 64.8141 1.52197 -6.53 -.62 -1.61 1.47 3.5 .03 .92
1 381 6.613501 4.3503 64.8141 1.52197 4.44 2.1 .46 -.75 .62 0 .92
1 382 7.874167 4.3503 64.8141 1.52197 -.04 2.02 -.82 1.68 -1.21 .13 .92
1 383 -4.3738112 4.3503 64.8141 1.52197 -1.25 -.56 1.47 .01 1.87 .17 .92
1 384 4.922349 4.3503 64.8141 1.52197 4.92 -2.76 -.42 1.43 -1.82 .18 .92
1 385 5.951562 4.3503 64.8141 1.52197 1.83 1.51 -1.59 .57 -.85 .18 .92
1 386 5.117455 4.3503 64.8141 1.52197 -8.06 1.21 -1.1 1.67 .76 .17 .92
1 387 -6.533714 4.3503 64.8141 1.52197 -2.79 -.06 2.09 -1.47 3.6 .15 .92
1 388 -8.706715 4.3503 64.8141 1.52197 -2.1 -.23 .15 1.14 2.3 .13 .92
1 389 -11.125276 4.3503 64.8141 1.52197 -12.28 -.78 2.44 -.47 6.01 .15 .92
1 390 -4.796246 1.77112 135.975 -.211201 -8.55 1.59 -4.62 .88 -1.99 0 .921
1 391 3.712848 1.77112 135.975 -.211201 -2.1 -.56 -2.19 3.42 -.67 0 .921
1 392 3.7155445 1.77112 135.975 -.211201 3.64 -2.46 -1.35 .95 -.52 0 .921
1 393 2.2699454 1.77112 135.975 -.211201 1.93 .85 .79 .95 -.44 .01 .921
1 394 -.0902156 1.77112 135.975 -.211201 -9.93 .49 -4.14 2.4 -1.79 .01 .921
1 395 10.217355 1.77112 135.975 -.211201 7.27 -1.25 0 -.5 -2.93 .02 .921
1 396 7.600992 1.77112 135.975 -.211201 11.41 2.05 2.75 .55 -4.06 .01 .921
1 397 10.264106 1.77112 135.975 -.211201 9.95 1.51 .03 -.34 -3.72 0 .921
1 398 3.92436 1.77112 135.975 -.211201 -.26 2.72 -1.58 .07 .53 .01 .921
1 399 5.984081 1.77112 135.975 -.211201 8.6 -.72 3.93 .1 .34 .01 .921
1 400 5.70101 1.77112 135.975 -.211201 3.88 1.51 4.59 -1.56 1.61 .01 .921
1 401 7.508363 1.77112 135.975 -.211201 4.46 1.84 .55 .91 -.75 .01 .921
1 402 -4.989778 2.91834 126.743 .595354 -3.73 2.59 -.06 -.03 1.05 0 .923
1 403 4.062807 2.91834 126.743 .595354 3.94 -.14 -1.23 1.15 .15 .01 .923
1 404 1.1356766 2.91834 126.743 .595354 -2.17 2 -.74 -.08 .91 .01 .923
1 405 10.845475 2.91834 126.743 .595354 7.6 2.01 1.79 -2.12 1.57 .01 .923
1 406 -.8442482 2.91834 126.743 .595354 1.29 .1 .14 -.39 -.62 0 .923
1 407 7.993305 2.91834 126.743 .595354 6.24 .23 3.07 -.32 -.13 .01 .923
1 408 3.181489 2.91834 126.743 .595354 .48 3.96 .82 .81 .74 .01 .923
1 409 -10.058084 2.91834 126.743 .595354 -9.54 -.29 -2.49 .02 .18 .01 .923
1 410 .4347875 2.91834 126.743 .595354 -3.15 -.05 -2.69 .23 -.26 .01 .923
1 411 8.028027 2.91834 126.743 .595354 8.21 -1.45 1.29 -.55 1.81 .01 .923
1 412 .5102112 2.91834 126.743 .595354 -3.9 -.35 -1.92 1.55 -2.76 .01 .923
1 413 9.997296 2.91834 126.743 .595354 9.85 1.35 -1.6 -.34 1.3 .01 .923
1 414 -4.2997527 3.30385 85.7129 1.05575 2.13 -1.37 2.81 -.76 1.66 .01 .925
1 415 10.53733 3.30385 85.7129 1.05575 10.01 -2.38 -1.92 -.22 -.98 0 .925
1 416 -3.642836 3.30385 85.7129 1.05575 -2.64 -1.01 -.75 1.43 .55 0 .925
1 417 2.704172 3.30385 85.7129 1.05575 -.44 -.27 1.81 .25 1.44 0 .925
1 418 1.5008903 3.30385 85.7129 1.05575 3.49 .11 .47 -.69 .14 .01 .925
1 419 1.354322 3.30385 85.7129 1.05575 -.68 1.29 -1.58 1.72 -.22 .01 .925
1 420 7.289534 3.30385 85.7129 1.05575 4.47 -.83 -1.83 1.37 -.11 0 .925
1 421 -.5046182 3.30385 85.7129 1.05575 -2.1 -.44 -1.33 1.95 -.63 0 .925
1 422 1.3661323 3.30385 85.7129 1.05575 -1.52 -.12 .49 1.85 -.43 0 .925

The data I am using is hedge fund returns by country and I am attempting to see if returns are affected depending on which country the fund is domiciled in.

If anyone is able to assist me with my problem it would be greatly appreciated

[Ken Chui]

Welcome to Statalist.

It'd be helpful to at least tell us:

1. The code of the model that you ran
2. The actual definition "having trouble doing so." Did you get an error? Did the beta showing up as omitted? Etc.

[David Connaughton]

Thanks for the quick reply Ken. I ran a regression of

by Dom: reg w_rollingperformance MktRF SMB HML.

This gives me the overall coefficient for each independent variable per country but what I am looking for is each coefficient of each independent variable per monthly return and to store these coefficients as separate variables in the dataset if that makes sense? So I didn't get an error message I am just unsure on what to do to get this information from the dataset.

[Ken Chui]

Still not too sure about the question, because I didn't see anything related "per monthly return." I did see a date but it's not formatted as something Stata can interpret.

Anyhow, a command that is useful for running many regressions followed by saving the outputs as a data set is statsby. You can use help statsby to learn more. Here is an example using a built-in dataset:

sysuse nlsw88, clear

* Let's say this is the model:
reg wage i.race age hours

* To loop this over "grade" and export the coefficient:
statsby _b[2.race] _b[3.race] _b[age] _b[hours] _b[_cons], by(grade): reg wage i.race age hours

* And if you are not sure what the coefficients are called, run:
reg wage i.race age hours, coeflegend

So, with very limited understanding on my side, I'm guessing something like this:

statsby _b[w_rollingperformance] _b[MktRF] _b[SMB] _b[HML] _b[_cons], by(Dom): reg w_rollingperformance MktRF SMB HML

[David Connaughton]

Hi Ken,

The code you provided despite my poor explanation ran and produced the following data:

input long Dom float(_stat_1 _stat_2 _stat_3 _stat_4)
1 .7606097 .4882778 -.13157566 .8394325
2 .4570877 -.025453834 -.2120535 -.010856646
3 .4002615 .27843446 .016526276 .4432861
4 .4369325 .3544034 -.2691492 .9848767
5 .5247886 .4671575 1.0518899 -1.9135516
6 .5419511 .4591544 .16145937 .8858464
7 .33429125 .2849044 .04186478 .8711806
8 .3391567 .03785133 -.3107762 .92203
9 .0491387 -.04772006 -.07109444 .4048271
10 .26298115 .08810613 -.022207905 .6787176
11 .4222111 .42271546 .2172042 -.12848902
12 .7372836 -.5507823 -.2066535 -.5550488
13 .2045164 .11067571 .07496166 .3962315
14 .42456084 .07935928 .15759166 .10828275
15 .7787618 .4123337 .14544873 .8992686
16 .11616552 -.15797202 -.005183562 .167129
17 .3818074 .1910281 -.09378766 .3186618
18 .6544379 .12404907 -.09363513 .15331326
19 .7317508 .6366329 -.15222113 .7161612
20 .22465764 .03939031 .25117952 .7018836
21 .5675365 .13457063 .23671393 -.55113524
22 .6590059 -.03825416 .13320667 -1.1545677
23 .0036441644 .02182176 -.05695591 1.501278
24 .3282415 .5471766 .028988555 .57576627
25 .6693143 .58366 .26450413 1.1079538
26 .6190763 .14841492 .05056467 -.08843616
27 .655951 -.028213607 -.04892578 -.09886978
28 .5197417 .07836565 -.08951684 .39356685
29 1.4791453 -1.533631 1.0185724 -4.845376
30 .009847125 -.08247767 .016942294 .3594066
31 .3526996 .21079268 .011250583 .8478047

This gives me the coefficients for each independent variable for each of the 31 countries I am examining. This is great but I'll try to explain which data I need a bit more clearly. The code you provided has given me the coefficients per country per each monthly return whereas I need the coefficients per date per each monthly return if that makes sense. But when I substitute Date for Dom in the above code it doesn't return the coefficients. Is this perhaps because I do not have the dates formatted correctly?

Apologies again if it is confusing to understand it's quite tricky to try and explain.

[Ken Chui]

1. So, when the "date" variable says 422 what does that mean? 422nd month?
2. Also, can you more clearly describe what happened with "it doesn't return the coefficients"? Did you get an error or did they look empty? On this forum, the exact description of unexpected outcome is always necessary.

[David Connaughton]

422 is the date 2011-06 but it is actually the 422nd month from the start date of my dataset which is 1977-10. As for "it doesn't return the coefficients" the data returned is as follows:

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155 . . . .
156 . . . .
157 0 0 0 -1.7882607
158 0 0 0 2.54302
159 0 0 0 2.2372997
160 . . . .
161 . . . .
162 . . . .
163 . . . .
164 . . . .
165 0 0 0 .9223845
166 0 0 0 -2.750789
167 0 0 0 -.5329759
168 0 0 0 3.675598
169 0 0 0 3.469343
170 0 0 0 -2.86486
171 0 0 0 8.605598
172 0 0 0 5.624561
173 0 0 0 7.907956
174 0 0 0 1.0929916
175 0 0 0 3.4425354
176 0 0 0 -5.003806
177 0 0 0 3.464592
178 0 0 0 3.7263556
179 0 0 0 1.325958
180 0 0 0 .6767432
181 0 0 0 -.1884735
182 0 0 0 5.136836
183 0 0 0 -.7475795
184 0 0 0 .7643068
185 0 0 0 .13028169
186 0 0 0 -1.9973378
187 0 0 0 1.6883205
188 0 0 0 2.993472
189 0 0 0 4.1467605
190 0 0 0 -.03982977
191 0 0 0 .23740634
192 0 0 0 .7313796
193 0 0 0 2.3498168
194 0 0 0 1.4658573
195 0 0 0 8.068813
196 0 0 0 1.3032956
197 0 0 0 2.2360525
198 0 0 0 1.869946
199 0 0 0 3.409041
200 0 0 0 2.5604045
201 0 0 0 4.1142373
202 0 0 0 2.7943025
203 0 0 0 1.7820222
204 0 0 0 1.0140516
205 0 0 0 -1.0320792
206 0 0 0 .7496175
207 0 0 0 -.6296057
208 0 0 0 -.9281559
209 0 0 0 -.10787938
210 0 0 0 -1.482721
211 0 0 0 .8978804
212 0 0 0 -.4031293
213 0 0 0 1.6119574
214 0 0 0 2.45986
215 0 0 0 2.855752
216 0 0 0 -2.508358
217 0 0 0 -1.0075419
218 0 0 0 .8220685
219 0 0 0 6.132732
220 0 0 0 1.8127472
221 0 0 0 4.6486306
222 0 0 0 2.437518
223 0 0 0 4.1680045
224 0 0 0 3.196016
225 0 0 0 1.4177116
226 0 0 0 -.29571545
227 0 0 0 .4391829
228 0 0 0 -1.0025383
229 0 0 0 3.404525
230 0 0 0 1.954853
231 0 0 0 -.04437708
232 0 0 0 .25902653
233 0 0 0 1.7052687
234 0 0 0 6.546594
235 0 0 0 .4344779
236 0 0 0 1.721917
237 0 0 0 -2.729549
238 0 0 0 1.702263
239 0 0 0 2.580531
240 0 0 0 4.697784
241 0 0 0 -1.9654526
242 0 0 0 -2.2154622
243 0 0 0 2.4854536
244 0 0 0 .687387
245 0 0 0 -.6463294
246 0 0 0 -.834232
247 0 0 0 2.952951
248 0 0 0 4.0598655
249 0 0 0 5.81165
250 0 0 0 -1.723186
251 0 0 0 3.66013
252 0 0 0 -1.4365523
253 0 0 0 1.8131993
254 0 0 0 1.3383964
255 0 0 0 3.9896536
256 0 0 0 3.622138
257 0 0 0 4.0068293
258 0 0 0 .6967539
259 0 0 0 -1.0241596
260 0 0 0 -.6127998
261 0 0 0 1.6840285
262 0 0 0 .18067354
263 0 0 0 4.79304
264 0 0 0 .1759425
265 0 0 0 -2.2043295
266 0 0 0 4.456694
267 0 0 0 6.616582
268 0 0 0 1.5001192
269 0 0 0 3.190824
270 0 0 0 2.6339765
271 0 0 0 1.8066647
272 0 0 0 3.305046
273 0 0 0 .8451826
274 0 0 0 1.801719
275 0 0 0 2.687631
276 0 0 0 1.56574
277 0 0 0 -2.222431
278 0 0 0 .5481677
279 0 0 0 4.781434
280 0 0 0 4.128241
281 0 0 0 .496241
282 0 0 0 -2.6174755
283 0 0 0 -1.0750346
284 0 0 0 3.629368
285 0 0 0 -1.1345185
286 0 0 0 2.586594
287 0 0 0 -1.3876655
288 0 0 0 2.578124
289 0 0 0 1.9651012
290 0 0 0 .26793197
291 0 0 0 .1631698
292 0 0 0 -1.437533
293 0 0 0 -.6682289
294 0 0 0 -.16263877
295 0 0 0 1.359217
296 0 0 0 .39885345
297 0 0 0 -.7021209
298 0 0 0 .3358777
299 0 0 0 -1.2870698
300 0 0 0 .8671119
301 0 0 0 .3107343
302 0 0 0 1.7938036
303 0 0 0 2.870297
304 0 0 0 .23990548
305 0 0 0 2.4199855
306 0 0 0 2.2893777
307 0 0 0 2.464903
308 0 0 0 1.4972696
309 0 0 0 -1.148742
310 0 0 0 1.821207
311 0 0 0 -.55624664
312 0 0 0 2.8431494
313 0 0 0 3.368915
314 0 0 0 1.8553407
315 0 0 0 5.42203
316 0 0 0 2.6187904
317 0 0 0 -2.670999
318 0 0 0 4.4314513
319 0 0 0 6.54741
320 0 0 0 .623268
321 0 0 0 .8114111
322 0 0 0 .9441531
323 0 0 0 3.153743
324 . . . .
325 . . . .
326 . . . .
327 0 0 0 .8263388
328 0 0 0 3.627918
329 0 0 0 7.066031
330 0 0 0 3.299406
331 0 0 0 4.001568
332 0 0 0 .3591135
333 0 0 0 -4.1382046
334 0 0 0 .12035535
335 0 0 0 -.05248924
336 0 0 0 -.19711477
337 0 0 0 -.24318385
338 0 0 0 3.887838
339 . . . .
340 . . . .
341 . . . .
342 0 0 0 -2.5647244
343 0 0 0 -3.1092696
344 0 0 0 3.651109
345 0 0 0 3.390483
346 0 0 0 3.724436
347 0 0 0 -1.5071877
348 0 0 0 -2.6320136
349 0 0 0 -.017039597
350 0 0 0 2.0816758
351 0 0 0 2.38123
352 0 0 0 2.1988816
353 0 0 0 2.56535
354 0 0 0 5.909122
355 0 0 0 3.768249
356 0 0 0 3.873747
357 0 0 0 2.999034
358 0 0 0 -.6891288
359 0 0 0 3.8538575
360 0 0 0 5.897731
361 0 0 0 -1.566509
362 0 0 0 -1.962781
363 0 0 0 -1.527363
364 0 0 0 2.0638754
365 0 0 0 .53817505
366 0 0 0 1.1063833
367 0 0 0 4.700454
368 0 0 0 -1.429755
369 0 0 0 .8875386
370 0 0 0 -.1925897
371 0 0 0 .9485995
372 0 0 0 5.177398
373 0 0 0 2.858654
374 0 0 0 1.7110724
375 0 0 0 .0957566
376 0 0 0 -2.0871358
377 0 0 0 5.909588
378 0 0 0 -3.0552435
379 0 0 0 -6.834568
380 0 0 0 -1.441031
381 0 0 0 4.7012277
382 0 0 0 3.9750135
383 0 0 0 -.8384295
384 0 0 0 1.651497
385 0 0 0 .8495483
386 0 0 0 -.4086961
387 0 0 0 -2.825472
388 0 0 0 -4.3241878
389 0 0 0 -6.69053
390 0 0 0 -3.535351
391 0 0 0 .8467969
392 0 0 0 4.492511
393 0 0 0 -1.2885823
394 0 0 0 -1.72348
395 0 0 0 4.2685666
396 0 0 0 4.08837
397 0 0 0 6.725787
398 0 0 0 .6768551
399 0 0 0 4.1910324
400 0 0 0 2.319998
401 0 0 0 4.278198
402 0 0 0 -2.1556764
403 0 0 0 3.182894
404 0 0 0 -2.3384478
405 0 0 0 5.402298
406 0 0 0 .14581004
407 0 0 0 3.81223
408 0 0 0 .6246623
409 0 0 0 -6.023701
410 0 0 0 -.51842165
411 0 0 0 4.964925
412 0 0 0 .3737543
413 0 0 0 7.010422
414 0 0 0 .29746783
415 0 0 0 3.6710236
416 0 0 0 -3.170248
417 0 0 0 -1.8278114
418 0 0 0 2.621568
419 0 0 0 1.418016
420 0 0 0 4.989111
421 0 0 0 -2.799564
422 0 0 0 -.8379345
423 0 0 0 .9331465
424 0 0 0 -2.0574691
425 0 0 0 -6.547884
426 0 0 0 2.849659
427 0 0 0 -.3872715
428 0 0 0 .504855
429 0 0 0 2.629084
430 0 0 0 3.338702
431 0 0 0 -1.3835152
432 0 0 0 -.12255343
433 0 0 0 -4.119591
434 0 0 0 1.5882108
435 0 0 0 .384168
436 0 0 0 1.2711047
437 0 0 0 2.344106
438 0 0 0 2.915382
439 0 0 0 2.1089876
440 0 0 0 .16866413
441 0 0 0 1.3753067
442 0 0 0 -1.0148652
443 0 0 0 -.003113585
444 0 0 0 2.2037373
445 0 0 0 -1.3299094
446 0 0 0 -1.436324
447 0 0 0 1.8174446
448 0 0 0 -.9061907
449 0 0 0 2.972715
450 0 0 0 -2.0557425
451 0 0 0 -.26809016
452 0 0 0 .9360279
453 0 0 0 -1.6520773
454 0 0 0 3.05618
455 0 0 0 .6424983
456 0 0 0 .4711663
457 0 0 0 .7131944
458 0 0 0 1.1763971
459 0 0 0 -1.4196012
460 0 0 0 .8481898
461 0 0 0 -1.974276
462 0 0 0 -1.0547433
463 0 0 0 1.3764433
464 0 0 0 -.8323529
465 0 0 0 .2393252
466 0 0 0 1.3670506
467 0 0 0 -1.172363
468 0 0 0 2.629783
469 0 0 0 -.4615532
470 0 0 0 -.50221497
471 0 0 0 -.2840908
472 0 0 0 -2.688898
473 0 0 0 -.8637602
474 0 0 0 -2.3398519
475 0 0 0 -1.9999788
476 0 0 0 -2.0204964
477 0 0 0 .5824781
478 0 0 0 .1497273
479 0 0 0 4.941984
480 0 0 0 .9625029
481 0 0 0 -1.502559
482 0 0 0 .9813345
483 0 0 0 2.1083226
484 0 0 0 .11741727
485 0 0 0 1.0789403
486 0 0 0 2.3974185
487 0 0 0 .6348416
488 0 0 0 1.5645576
489 0 0 0 2.2789338
490 0 0 0 .984327
491 0 0 0 .5972265
492 0 0 0 1.1730032
493 0 0 0 1.6102564
494 0 0 0 .7447472
495 0 0 0 2.7202535
496 0 0 0 1.2084086
497 0 0 0 -.13759907
498 0 0 0 4.0901747
499 0 0 0 -4.1596274
500 0 0 0 -.069970235
501 0 0 0 -1.4353913
502 0 0 0 -3.0630674
503 0 0 0 -.9528926
504 0 0 0 -.3255192
505 0 0 0 -1.281682
506 0 0 0 -1.1430699
507 0 0 0 1.0492007
508 0 0 0 -.7564256
509 0 0 0 -.17305963
510 0 0 0 2.964526
511 0 0 0 .9793618
512 0 0 0 .3299157
513 0 0 0 2.420127
514 0 0 0 .8825688
515 0 0 0 .6136953
516 0 0 0 1.0226053
517 0 0 0 -1.1931114
518 0 0 0 3.0183804
519 0 0 0 -.7044319
520 0 0 0 -.784647
521 0 0 0 -.16522866
522 0 0 0 -.3673746
523 0 0 0 -.12847112
524 0 0 0 5.51447
525 0 0 0 4.2941794
526 0 0 0 -2.496754
527 0 0 0 -5.34657
528 0 0 0 3.715836
529 0 0 0 2.457945
530 0 0 0 2.4089625
531 0 0 0 4.748903
532 0 0 0 2.303772
533 0 0 0 -2.1994183
534 0 0 0 .3994827
535 0 0 0 1.7981707
536 0 0 0 -3.121339
537 0 0 0 1.2726983
538 0 0 0 1.8873994
539 0 0 0 -.3080572
540 0 0 0 2.855128
541 0 0 0 1.9366882
542 0 0 0 -1.1565744
543 0 0 0 .1882932
544 0 0 0 .9578025
545 0 0 0 -1.6448677
546 0 0 0 -1.514066
547 0 0 0 -.4175013
548 . . . .

With 1 being the first date in the dataset 1977-10 and 548 being the last date 2022-03.

Thank you again for your help.

[Ken Chui]

You can try:

reg w_rollingperformance MktRF SMB HML if date==1

and see how many observations are in the regression model (it should appear at the top right). My guess is that you have fewer number of countries with records dated back that early than the model can afford. You have four betas + intercept which means you'll need at least 4+1+1 = 6 countries, an extra +1 for the error degree of freedom.

As for why all the betas are zero, usually it's because they have the same dependent variable value. Aka, all the countries share the same MktRF within each month, creating a flat regression line. You can check with

tabstat w_rollingperformance if inlist(date, 500, 510, 520, 530), by(date) stat(mean sd min max)

[David Connaughton]

When I run the first code I get this error: reg w_rollingperformance MktRF SMB HML if date==1 no observations

As for the second code, it produced the following table

date | Mean SD Min Max
----------+----------------------------------------
2018-011 | -.0699702 2.095089 -3.680611 5.909188
2019-01 | 2.964526 3.081287 -1.718028 11.9062
2019-08 | -.784647 2.531177 -6.425319 5.14885
2020-06 | 2.408963 2.350614 -1.418417 9.307755
----------+----------------------------------------
Total | 1.141039 2.968849 -6.425319 11.9062
---------------------------------------------------

[Ken Chui]

The first says "no observations" which means the data do not have any valid cases for those time points, thus there were no regression models.

The second is odd because it looks like you do have variability in the dependent variable. Could you perhaps post the full data for a certain month, like: dataex date w_rollingperformance MktRF SMB HML if date==540? That way users here can try run the regression and investigate why all the betas showed "0".

[David Connaughton]

540 4.1315246 4.49 -1.22 -1.97
540 2.8121884 4.49 -1.22 -1.97
540 5.311934 4.49 -1.22 -1.97
540 5.311923 4.49 -1.22 -1.97
540 2.749865 4.49 -1.22 -1.97
540 3.8946686 4.49 -1.22 -1.97
540 .4133448 4.49 -1.22 -1.97
540 3.946039 4.49 -1.22 -1.97
540 3.892389 4.49 -1.22 -1.97
540 4.0171 4.49 -1.22 -1.97
540 2.0891922 4.49 -1.22 -1.97
540 2.394556 4.49 -1.22 -1.97
540 2.794036 4.49 -1.22 -1.97
540 2.1746635 4.49 -1.22 -1.97
540 4.019618 4.49 -1.22 -1.97
540 3.64831 4.49 -1.22 -1.97
540 2.7158375 4.49 -1.22 -1.97
540 3.000491 4.49 -1.22 -1.97
540 2.9321954 4.49 -1.22 -1.97
540 2.271261 4.49 -1.22 -1.97
540 .4512209 4.49 -1.22 -1.97
540 3.302429 4.49 -1.22 -1.97
540 1.52 4.49 -1.22 -1.97
540 1.169814 4.49 -1.22 -1.97
540 .11628571 4.49 -1.22 -1.97
540 2.3228958 4.49 -1.22 -1.97
540 3.8977385 4.49 -1.22 -1.97
540 5.497185 4.49 -1.22 -1.97
540 0 4.49 -1.22 -1.97

[Ken Chui]

To have your data format correctly so that other people can use, next time, please post the lines with "CODE" till ending line with "CODE".

540 4.1315246 4.49 -1.22 -1.97
540 2.8121884 4.49 -1.22 -1.97
540 5.311934 4.49 -1.22 -1.97
540 5.311923 4.49 -1.22 -1.97
540 2.749865 4.49 -1.22 -1.97
540 3.8946686 4.49 -1.22 -1.97
540 .4133448 4.49 -1.22 -1.97
540 3.946039 4.49 -1.22 -1.97
540 3.892389 4.49 -1.22 -1.97
540 4.0171 4.49 -1.22 -1.97
540 2.0891922 4.49 -1.22 -1.97
540 2.394556 4.49 -1.22 -1.97
540 2.794036 4.49 -1.22 -1.97
540 2.1746635 4.49 -1.22 -1.97
540 4.019618 4.49 -1.22 -1.97
540 3.64831 4.49 -1.22 -1.97
540 2.7158375 4.49 -1.22 -1.97
540 3.000491 4.49 -1.22 -1.97
540 2.9321954 4.49 -1.22 -1.97
540 2.271261 4.49 -1.22 -1.97
540 .4512209 4.49 -1.22 -1.97
540 3.302429 4.49 -1.22 -1.97
540 1.52 4.49 -1.22 -1.97
540 1.169814 4.49 -1.22 -1.97
540 .11628571 4.49 -1.22 -1.97
540 2.3228958 4.49 -1.22 -1.97
540 3.8977385 4.49 -1.22 -1.97
540 5.497185 4.49 -1.22 -1.97
540 0 4.49 -1.22 -1.97

The issue that you got no estimate is the other side being constant. In regression analysis, the independent variables cannot be a constant.

      Source |       SS           df       MS      Number of obs   =        29
-------------+----------------------------------   F(1, 27)        =         .
       Model |           0         1           0   Prob > F        =         .
    Residual |           0        27           0   R-squared       =         .
-------------+----------------------------------   Adj R-squared   =         .
       Total |           0        28           0   Root MSE        =         0

------------------------------------------------------------------------------
           y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          x1 |          0  (omitted)
          x2 |          0  (omitted)
          x3 |          0  (omitted)
       _cons |        540          .        .       .            .           .
------------------------------------------------------------------------------

Essentially, we cannot model vertical regression line. You may have to investigate why those values are the same across country within month. And may have to consider revising those numbers separately by country.