Panel data xtset

[Sofia Moon]

Hello

I have panel data and try to have fixed effects regression as below

averagemonthlyincome2 ~ oil + index + marketshare + timefixedeffect + cityfixedeffect

where averagemonthlyincome2 is based on id. and I would like to see the city level effects.

To run fixed effects regression, I tried

xtset city year

Then STATA says

repeated time values within panel

So I tried

xtset id year

The STATA says

. xtset id year
panel variable: id (weakly balanced)
time variable: year, 2008 to 2019
delta: 1 unit


And the I runned

xtreg averagemonthlyincome2 index marketshare oil, fe

Then I get the result as below, which is not meaningful.

note: bdiy omitted because of collinearity
note: marketshare omitted because of collinearity
note: crudeoilprice omitted because of collinearity

Fixed-effects (within) regression Number
> of obs
> =
> 4,867,396
Group variable: id Number
> of groups
> =
> 4,867,396

R-sq: Obs pe
> r group:
within = .
> min
> =
> 1
between = . avg = 1.0
overall = . max = 1

F(0,0) = 0.00
corr(u_i, Xb) = . Prob > F = .

-------------------------------------------------------------------------------
averagemont~2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------+----------------------------------------------------------------
bdiy | 0 (omitted)
marketshare | 0 (omitted)
crudeoilprice | 0 (omitted)
_cons | 72.48875 . . . . .
--------------+----------------------------------------------------------------
sigma_u | 131.54781
sigma_e | .
rho | . (fraction of variance due to u_i)
-------------------------------------------------------------------------------
F test that all u_i=0: F(4867395, 0) = . Prob > F = .

.
end of do-file



So my question is, is it right to xtset id instead of city?
Or is there any way to make set with city and year?




my data set is as below
input int(year city) byte industry double marketshare float index long id float(averagemonthlyincome2 meanaveragemonthlyincome growthmeanaveragemonthlyincome meanaveragemonthlyincome2 growthmeanaveragemonthlyincome2) double oil
2008 1100 59 .3738310647431195 6390.299 1348830 200 84.28888 . 72.61469 . 99.67
2008 3743 85 .3738310647431195 6390.299 4266579 270 60.06294 . 51.71675 . 99.67
2008 3337 47 .3738310647431195 6390.299 4578811 0 62.36494 . 51.71675 . 99.67
2008 3119 . .3738310647431195 6390.299 4460993 0 95.41734 . 51.71675 . 99.67
2008 2100 . .3738310647431195 6390.299 1137511 0 61.82993 . 72.61469 . 99.67
2008 3633 1 .3738310647431195 6390.299 4655133 0 29.17916 . 51.71675 . 99.67
2008 2300 25 .3738310647431195 6390.299 563534 180 67.57023 . 72.61469 . 99.67
2008 3204 84 .3738310647431195 6390.299 1585400 350 58.49656 . 51.71675 . 99.67
2008 3127 . .3738310647431195 6390.299 2221714 0 47.29664 . 51.71675 . 99.67
2008 3332 1 .3738310647431195 6390.299 2316464 0 26.590517 . 51.71675 . 99.67
2008 3406 . .3738310647431195 6390.299 2474013 0 32.731426 . 51.71675 . 99.67
2008 3840 . .3738310647431195 6390.299 1639828 0 19.99288 . 51.71675 . 99.67
2008 3107 . .3738310647431195 6390.299 4363813 0 74.73983 . 51.71675 . 99.67
2008 1100 86 .3738310647431195 6390.299 584728 120 84.28888 . 72.61469 . 99.67
2008 3110 . .3738310647431195 6390.299 4599651 0 87.05631 . 51.71675 . 99.67
2008 3109 . .3738310647431195 6390.299 4792032 0 78.16296 . 51.71675 . 99.67
2008 2500 . .3738310647431195 6390.299 1189131 0 77.35721 . 72.61469 . 99.67
2008 3119 17 .3738310647431195 6390.299 2387529 140 95.41734 . 51.71675 . 99.67
2008 2600 30 .3738310647431195 6390.299 224167 600 111.90633 . 111.90633 . 99.67
2008 3120 . .3738310647431195 6390.299 2871374 0 65.74023 . 51.71675 . 99.67
2008 3639 56 .3738310647431195 6390.299 3573945 0 29.603773 . 51.71675 . 99.67
2008 3404 . .3738310647431195 6390.299 4713895 0 77.16308 . 51.71675 . 99.67
2008 3336 85 .3738310647431195 6390.299 3888252 830 26.992537 . 51.71675 . 99.67
2008 3704 . .3738310647431195 6390.299 4554308 0 48.97559 . 51.71675 . 99.67
2008 3138 1 .3738310647431195 6390.299 2580620 0 43.62741 . 51.71675 . 99.67
2008 3706 23 .3738310647431195 6390.299 1695943 0 45.64651 . 51.71675 . 99.67
2008 2100 . .3738310647431195 6390.299 888965 0 61.82993 . 72.61469 . 99.67
2008 3702 . .3738310647431195 6390.299 3401059 0 62.92089 . 51.71675 . 99.67
2008 2100 47 .3738310647431195 6390.299 1076354 0 61.82993 . 72.61469 . 99.67
2008 3538 61 .3738310647431195 6390.299 4575735 160 25.832664 . 51.71675 . 99.67
2008 3407 87 .3738310647431195 6390.299 1575341 0 62.89919 . 51.71675 . 99.67
2008 2200 . .3738310647431195 6390.299 1079778 0 57.66283 . 72.61469 . 99.67
2008 2100 . .3738310647431195 6390.299 1371321 0 61.82993 . 72.61469 . 99.67
2008 3606 3 .3738310647431195 6390.299 2387576 0 101.41905 . 51.71675 . 99.67
2008 3811 29 .3738310647431195 6390.299 2832955 400 72.649 . 72.649 . 99.67
2008 3811 . .3738310647431195 6390.299 4791049 0 72.649 . 72.649 . 99.67
2008 3701 47 .3738310647431195 6390.299 4627237 0 78.90858 . 51.71675 . 99.67
2008 2600 49 .3738310647431195 6390.299 192518 170 111.90633 . 111.90633 . 99.67
2008 3811 49 .3738310647431195 6390.299 4033238 40 72.649 . 72.649 . 99.67
2008 3738 . .3738310647431195 6390.299 3264709 0 16.790682 . 51.71675 . 99.67
2008 3808 1 .3738310647431195 6390.299 4772163 0 39.40766 . 51.71675 . 99.67
2008 3123 91 .3738310647431195 6390.299 4464775 0 74.73311 . 51.71675 . 99.67
2008 1100 47 .3738310647431195 6390.299 1021661 0 84.28888 . 72.61469 . 99.67
2008 2200 . .3738310647431195 6390.299 534834 0 57.66283 . 72.61469 . 99.67
2008 3604 . .3738310647431195 6390.299 4731906 0 30.519444 . 51.71675 . 99.67
2008 3808 . .3738310647431195 6390.299 1944908 0 39.40766 . 51.71675 . 99.67
2008 3337 1 .3738310647431195 6390.299 4689997 0 62.36494 . 51.71675 . 99.67
2008 3109 . .3738310647431195 6390.299 1585307 0 78.16296 . 51.71675 . 99.67
2008 3733 1 .3738310647431195 6390.299 2387606 0 28.542027 . 51.71675 . 99.67
2008 3116 52 .3738310647431195 6390.299 4469176 400 97.669 . 51.71675 . 99.67
2008 3202 . .3738310647431195 6390.299 3262727 0 57.88957 . 51.71675 . 99.67
2008 2600 75 .3738310647431195 6390.299 244319 260 111.90633 . 111.90633 . 99.67
2008 3706 . .3738310647431195 6390.299 2652414 0 45.64651 . 51.71675 . 99.67
2008 1100 49 .3738310647431195 6390.299 1069527 250 84.28888 . 72.61469 . 99.67
2008 3502 87 .3738310647431195 6390.299 88331 120 60.92495 . 60.92495 . 99.67
2008 3337 . .3738310647431195 6390.299 1809696 0 62.36494 . 51.71675 . 99.67
2008 3706 85 .3738310647431195 6390.299 1942550 240 45.64651 . 51.71675 . 99.67
2008 2600 85 .3738310647431195 6390.299 228787 80 111.90633 . 111.90633 . 99.67
2008 3137 85 .3738310647431195 6390.299 4776714 150 44.0325 . 51.71675 . 99.67
2008 3506 . .3738310647431195 6390.299 2346607 0 28.67534 . 51.71675 . 99.67
2008 3806 96 .3738310647431195 6390.299 3185836 0 57.12525 . 51.71675 . 99.67
2008 3707 . .3738310647431195 6390.299 2387659 0 33.05228 . 51.71675 . 99.67
2008 3737 56 .3738310647431195 6390.299 3339692 0 26.6216 . 51.71675 . 99.67
2008 2600 96 .3738310647431195 6390.299 223343 0 111.90633 . 111.90633 . 99.67
2008 3833 41 .3738310647431195 6390.299 1772705 200 22.267694 . 51.71675 . 99.67
2008 1100 97 .3738310647431195 6390.299 1017273 0 84.28888 . 72.61469 . 99.67
2008 3236 . .3738310647431195 6390.299 1630117 0 31.343794 . 51.71675 . 99.67
2008 3834 . .3738310647431195 6390.299 71284 0 39.19793 . 39.19793 . 99.67
2008 3334 . .3738310647431195 6390.299 4644605 0 32.5542 . 51.71675 . 99.67
2008 3203 49 .3738310647431195 6390.299 2387674 0 62.04602 . 51.71675 . 99.67
2008 3111 85 .3738310647431195 6390.299 4590377 400 115.73279 . 51.71675 . 99.67
2008 3900 35 .3738310647431195 6390.299 3888154 200 60.12286 . 51.71675 . 99.67
2008 3303 . .3738310647431195 6390.299 4765636 0 55.21495 . 51.71675 . 99.67
2008 3405 85 .3738310647431195 6390.299 1691051 0 62.4413 . 51.71675 . 99.67
2008 3733 47 .3738310647431195 6390.299 4790547 85 28.542027 . 51.71675 . 99.67
2008 3301 42 .3738310647431195 6390.299 4773384 0 78.74268 . 51.71675 . 99.67
2008 3107 . .3738310647431195 6390.299 2432734 0 74.73983 . 51.71675 . 99.67
2008 3121 71 .3738310647431195 6390.299 2387717 500 74.732124 . 51.71675 . 99.67
2008 3332 1 .3738310647431195 6390.299 3956579 0 26.590517 . 51.71675 . 99.67
2008 3203 56 .3738310647431195 6390.299 3426094 120 62.04602 . 51.71675 . 99.67
2008 2600 84 .3738310647431195 6390.299 256882 330 111.90633 . 111.90633 . 99.67
2008 3433 87 .3738310647431195 6390.299 2387721 160 27.606874 . 51.71675 . 99.67
2008 3302 . .3738310647431195 6390.299 3883126 0 55.63776 . 51.71675 . 99.67
2008 3236 1 .3738310647431195 6390.299 2387734 0 31.343794 . 51.71675 . 99.67
2008 3701 3 .3738310647431195 6390.299 4592802 0 78.90858 . 51.71675 . 99.67
2008 3733 . .3738310647431195 6390.299 4639014 0 28.542027 . 51.71675 . 99.67
2008 1100 . .3738310647431195 6390.299 1347640 0 84.28888 . 72.61469 . 99.67
2008 3732 . .3738310647431195 6390.299 4762439 0 20.89412 . 51.71675 . 99.67
2008 3900 42 .3738310647431195 6390.299 4621050 80 60.12286 . 51.71675 . 99.67
2008 3643 . .3738310647431195 6390.299 4693667 0 14.283452 . 51.71675 . 99.67
2008 3116 28 .3738310647431195 6390.299 3506308 0 97.669 . 51.71675 . 99.67
2008 3106 14 .3738310647431195 6390.299 4707802 110 77.70949 . 51.71675 . 99.67
2008 3302 56 .3738310647431195 6390.299 2387757 150 55.63776 . 51.71675 . 99.67
2008 2200 . .3738310647431195 6390.299 909861 0 57.66283 . 72.61469 . 99.67
2008 2500 . .3738310647431195 6390.299 876780 0 77.35721 . 72.61469 . 99.67
2008 3640 1 .3738310647431195 6390.299 115369 0 22.53673 . 22.53673 . 99.67
2008 2100 10 .3738310647431195 6390.299 897508 60 61.82993 . 72.61469 . 99.67
2008 3803 85 .3738310647431195 6390.299 4674363 400 59.61138 . 51.71675 . 99.67
2008 3900 . .3738310647431195 6390.299 3147257 0 60.12286 . 51.71675 . 99.67
2008 3434 29 .3738310647431195 6390.299 4678621 80 37.699547 . 51.71675 . 99.67
end


Thanks a lot!


Best,
Sofia

[Clyde Schechter]

What you should do is just -xtset city- with no specification of the time variable. For your purposes, there is no need to specify the time variable in -xtset-, even if you could. That is only required if you are going to look at autoregressive structure in the data or work with lags and leads. But none of that is even meaningful when there are multiple observations in the same city in a given year. So just -xtset -city-. Then run your -xtreg-. And since you say you want year fixed effects, you need to include i.year in the list of predictor variables in your -xtreg- command. (And that would be true even if you could -xtset city year-, because only the panel variable is automatically included by the -xt- commands. When you want to include the time variable, you have to do it explicitly regardless.)